Association analysis is a technique used to uncover relationships between items in transactional data. It involves finding frequent itemsets whose occurrence exceeds a minimum support threshold, and then generating association rules from these itemsets that satisfy minimum confidence. The Apriori algorithm is commonly used for this task, as it leverages the Apriori property to prune the search space - if an itemset is infrequent, its supersets cannot be frequent. It performs multiple database scans to iteratively grow frequent itemsets and extract high confidence rules.
This document discusses association rule mining. Association rule mining finds frequent patterns, associations, correlations, or causal structures among items in transaction databases. The Apriori algorithm is commonly used to find frequent itemsets and generate association rules. It works by iteratively joining frequent itemsets from the previous pass to generate candidates, and then pruning the candidates that have infrequent subsets. Various techniques can improve the efficiency of Apriori, such as hashing to count itemsets and pruning transactions that don't contain frequent itemsets. Alternative approaches like FP-growth compress the database into a tree structure to avoid costly scans and candidate generation. The document also discusses mining multilevel, multidimensional, and quantitative association rules.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
The document discusses frequent pattern mining and the Apriori algorithm. It introduces frequent patterns as frequently occurring sets of items in transaction data. The Apriori algorithm is described as a seminal method for mining frequent itemsets via multiple passes over the data, generating candidate itemsets and pruning those that are not frequent. Challenges with Apriori include multiple database scans and large number of candidate sets generated.
Data mining techniques can uncover useful patterns and relationships in data. Association rule mining finds frequent patterns and generates rules about associations between different attributes in the data. The Apriori algorithm is commonly used to efficiently find all frequent itemsets in a transaction database and generate association rules from those itemsets. It works in multiple passes over the data, generating candidate itemsets of length k from frequent itemsets of length k-1 and pruning unpromising candidates that have infrequent subsets.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
Association rule mining finds frequent patterns and correlations among items in transaction databases. It involves two main steps:
1) Frequent itemset generation: Finds itemsets that occur together in a minimum number of transactions (above a support threshold). This is done efficiently using the Apriori algorithm.
2) Rule generation: Generates rules from frequent itemsets where the confidence (fraction of transactions with left hand side that also contain right hand side) is above a minimum threshold. Rules are a partitioning of an itemset into left and right sides.
This document provides an introduction to data mining. It defines data mining as the process of extracting knowledge from large amounts of data. The document outlines the typical steps in the knowledge discovery process including data cleaning, transformation, mining, and evaluation. It also describes some common challenges in data mining like dealing with large, high-dimensional, heterogeneous and distributed data. Finally, it summarizes several common data mining tasks like classification, association analysis, clustering, and anomaly detection.
The document discusses sequential pattern mining, which involves finding frequently occurring ordered sequences or subsequences in sequence databases. It covers key concepts like sequential patterns, sequence databases, support count, and subsequences. It also describes several algorithms for sequential pattern mining, including GSP (Generalized Sequential Patterns) which uses a candidate generation and test approach, SPADE which works on a vertical data format, and PrefixSpan which employs a prefix-projected sequential pattern growth approach without candidate generation.
This document discusses association rule mining. Association rule mining finds frequent patterns, associations, correlations, or causal structures among items in transaction databases. The Apriori algorithm is commonly used to find frequent itemsets and generate association rules. It works by iteratively joining frequent itemsets from the previous pass to generate candidates, and then pruning the candidates that have infrequent subsets. Various techniques can improve the efficiency of Apriori, such as hashing to count itemsets and pruning transactions that don't contain frequent itemsets. Alternative approaches like FP-growth compress the database into a tree structure to avoid costly scans and candidate generation. The document also discusses mining multilevel, multidimensional, and quantitative association rules.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
The document discusses frequent pattern mining and the Apriori algorithm. It introduces frequent patterns as frequently occurring sets of items in transaction data. The Apriori algorithm is described as a seminal method for mining frequent itemsets via multiple passes over the data, generating candidate itemsets and pruning those that are not frequent. Challenges with Apriori include multiple database scans and large number of candidate sets generated.
Data mining techniques can uncover useful patterns and relationships in data. Association rule mining finds frequent patterns and generates rules about associations between different attributes in the data. The Apriori algorithm is commonly used to efficiently find all frequent itemsets in a transaction database and generate association rules from those itemsets. It works in multiple passes over the data, generating candidate itemsets of length k from frequent itemsets of length k-1 and pruning unpromising candidates that have infrequent subsets.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
Association rule mining finds frequent patterns and correlations among items in transaction databases. It involves two main steps:
1) Frequent itemset generation: Finds itemsets that occur together in a minimum number of transactions (above a support threshold). This is done efficiently using the Apriori algorithm.
2) Rule generation: Generates rules from frequent itemsets where the confidence (fraction of transactions with left hand side that also contain right hand side) is above a minimum threshold. Rules are a partitioning of an itemset into left and right sides.
This document provides an introduction to data mining. It defines data mining as the process of extracting knowledge from large amounts of data. The document outlines the typical steps in the knowledge discovery process including data cleaning, transformation, mining, and evaluation. It also describes some common challenges in data mining like dealing with large, high-dimensional, heterogeneous and distributed data. Finally, it summarizes several common data mining tasks like classification, association analysis, clustering, and anomaly detection.
The document discusses sequential pattern mining, which involves finding frequently occurring ordered sequences or subsequences in sequence databases. It covers key concepts like sequential patterns, sequence databases, support count, and subsequences. It also describes several algorithms for sequential pattern mining, including GSP (Generalized Sequential Patterns) which uses a candidate generation and test approach, SPADE which works on a vertical data format, and PrefixSpan which employs a prefix-projected sequential pattern growth approach without candidate generation.
Clustering is an unsupervised learning technique used to group unlabeled data points together based on similarities. It aims to maximize similarity within clusters and minimize similarity between clusters. There are several clustering methods including partitioning, hierarchical, density-based, grid-based, and model-based. Clustering has many applications such as pattern recognition, image processing, market research, and bioinformatics. It is useful for extracting hidden patterns from large, complex datasets.
Decision tree induction \ Decision Tree Algorithm with Example| Data scienceMaryamRehman6
This Decision Tree Algorithm in Machine Learning Presentation will help you understand all the basics of Decision Tree along with what Machine Learning is, what Machine Learning is, what Decision Tree is, the advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with resolved examples, and at the end of the decision Tree use case/demo in Python for loan payment. For both beginners and experts who want to learn Machine Learning Algorithms, this Decision Tree tutorial is perfect.
Data mining involves multiple steps in the knowledge discovery process including data cleaning, integration, selection, transformation, mining, and pattern evaluation. It has various functionalities including descriptive mining to characterize data, predictive mining for inference, and different mining techniques like classification, association analysis, clustering, and outlier analysis.
This document discusses time-series data and methods for analyzing it. Time-series data consists of sequential values measured over time that can be analyzed to identify patterns, trends, and outliers. Key methods discussed include trend analysis to identify long-term movements, seasonal variations, and irregular components; similarity search to find similar sequences; and dimensionality reduction and transformation techniques to reduce data size before analysis or indexing.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.
This document provides an overview of PAC (Probably Approximately Correct) learning theory. It discusses how PAC learning relates the probability of successful learning to the number of training examples, complexity of the hypothesis space, and accuracy of approximating the target function. Key concepts explained include training error vs true error, overfitting, the VC dimension as a measure of hypothesis space complexity, and how PAC learning bounds can be derived for finite and infinite hypothesis spaces based on factors like the training size and VC dimension.
The document discusses various clustering approaches including partitioning, hierarchical, density-based, grid-based, model-based, frequent pattern-based, and constraint-based methods. It focuses on partitioning methods such as k-means and k-medoids clustering. K-means clustering aims to partition objects into k clusters by minimizing total intra-cluster variance, representing each cluster by its centroid. K-medoids clustering is a more robust variant that represents each cluster by its medoid or most centrally located object. The document also covers algorithms for implementing k-means and k-medoids clustering.
This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It explains that supervised learning involves learning from labeled examples, unsupervised learning involves categorizing without labels, and reinforcement learning involves learning behaviors to achieve goals through interaction. The document also discusses regression vs classification problems, the learning and testing process, and examples of machine learning applications like customer profiling, face recognition, and handwritten character recognition.
Data mining is the process of automatically discovering useful information from large data sets. It draws from machine learning, statistics, and database systems to analyze data and identify patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk assessment, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.
The document describes the SPADE algorithm for sequential pattern mining. SPADE aims to improve efficiency over previous algorithms by reducing the number of database scans and using a more compact vertical id-list database format. It works by decomposing the search space into equivalence classes based on common prefixes and processing each class independently with depth-first search.
Association rule mining is used to find interesting relationships among data items in large datasets. It can help with business decision making by analyzing customer purchasing patterns. For example, market basket analysis looks at what items are frequently bought together. Association rules use support and confidence metrics, where support is the probability an itemset occurs and confidence is the probability that a rule is correct. The Apriori algorithm is commonly used to generate association rules by first finding frequent itemsets that meet a minimum support threshold across multiple passes of the data. It then generates rules from those itemsets if they meet a minimum confidence. Association rule mining has various applications and can provide useful insights but also has computational limitations.
Supervised learning and Unsupervised learning Usama Fayyaz
This document discusses supervised and unsupervised machine learning. Supervised learning uses labeled training data to learn a function that maps inputs to outputs. Unsupervised learning is used when only input data is available, with the goal of modeling underlying structures or distributions in the data. Common supervised algorithms include decision trees and logistic regression, while common unsupervised algorithms include k-means clustering and dimensionality reduction.
The document provides an overview of data mining concepts including association rules, classification, and clustering algorithms. It introduces data mining and knowledge discovery processes. Association rule mining aims to find relationships between variables in large datasets using the Apriori and FP-growth algorithms. Classification algorithms build a model to predict class membership for new records based on a decision tree. Clustering algorithms group similar records together without predefined classes.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Web mining uses data mining techniques to extract information from web documents and services. It involves web content mining of page content and search results, web structure mining of hyperlink structures, and web usage mining of server logs to find user access patterns. Data mining techniques like classification, clustering, and association rule mining can be applied to web data to discover useful patterns and information.
Association Rule Learning Part 1: Frequent Itemset GenerationKnoldus Inc.
A methodology useful for discovering interesting relationships hidden in large data sets. The uncovered relationships can be presented in the form of association rules.
This slide first introduces the sequential pattern mining problem and also presents some required definitions in order to understand GSP algorithm. At then end there is a brief introduction of GSP algorithm and some practical constraints which it supports.
This document discusses unsupervised machine learning classification through clustering. It defines clustering as the process of grouping similar items together, with high intra-cluster similarity and low inter-cluster similarity. The document outlines common clustering algorithms like K-means and hierarchical clustering, and describes how K-means works by assigning points to centroids and iteratively updating centroids. It also discusses applications of clustering in domains like marketing, astronomy, genomics and more.
The comparative study of apriori and FP-growth algorithmdeepti92pawar
This document summarizes a seminar presentation comparing the Apriori and FP-Growth algorithms for association rule mining. The document introduces association rule mining and frequent itemset mining. It then describes the Apriori algorithm, including its generate-and-test approach and bottlenecks. Next, it explains the FP-Growth algorithm, including how it builds an FP-tree to efficiently extract frequent itemsets without candidate generation. Finally, it provides results comparing the performance of the two algorithms and concludes that FP-Growth is more efficient for mining long patterns.
This document discusses association rule mining, which aims to find rules that predict the occurrence of items based on other items in transactions. It defines key concepts like frequent itemsets, support, confidence and association rules. It describes the Apriori algorithm for efficiently mining frequent itemsets by leveraging the anti-monotone property of support. The document also discusses challenges in applying association rule mining to categorical and continuous attributes in record data.
Clustering is an unsupervised learning technique used to group unlabeled data points together based on similarities. It aims to maximize similarity within clusters and minimize similarity between clusters. There are several clustering methods including partitioning, hierarchical, density-based, grid-based, and model-based. Clustering has many applications such as pattern recognition, image processing, market research, and bioinformatics. It is useful for extracting hidden patterns from large, complex datasets.
Decision tree induction \ Decision Tree Algorithm with Example| Data scienceMaryamRehman6
This Decision Tree Algorithm in Machine Learning Presentation will help you understand all the basics of Decision Tree along with what Machine Learning is, what Machine Learning is, what Decision Tree is, the advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with resolved examples, and at the end of the decision Tree use case/demo in Python for loan payment. For both beginners and experts who want to learn Machine Learning Algorithms, this Decision Tree tutorial is perfect.
Data mining involves multiple steps in the knowledge discovery process including data cleaning, integration, selection, transformation, mining, and pattern evaluation. It has various functionalities including descriptive mining to characterize data, predictive mining for inference, and different mining techniques like classification, association analysis, clustering, and outlier analysis.
This document discusses time-series data and methods for analyzing it. Time-series data consists of sequential values measured over time that can be analyzed to identify patterns, trends, and outliers. Key methods discussed include trend analysis to identify long-term movements, seasonal variations, and irregular components; similarity search to find similar sequences; and dimensionality reduction and transformation techniques to reduce data size before analysis or indexing.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.
This document provides an overview of PAC (Probably Approximately Correct) learning theory. It discusses how PAC learning relates the probability of successful learning to the number of training examples, complexity of the hypothesis space, and accuracy of approximating the target function. Key concepts explained include training error vs true error, overfitting, the VC dimension as a measure of hypothesis space complexity, and how PAC learning bounds can be derived for finite and infinite hypothesis spaces based on factors like the training size and VC dimension.
The document discusses various clustering approaches including partitioning, hierarchical, density-based, grid-based, model-based, frequent pattern-based, and constraint-based methods. It focuses on partitioning methods such as k-means and k-medoids clustering. K-means clustering aims to partition objects into k clusters by minimizing total intra-cluster variance, representing each cluster by its centroid. K-medoids clustering is a more robust variant that represents each cluster by its medoid or most centrally located object. The document also covers algorithms for implementing k-means and k-medoids clustering.
This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It explains that supervised learning involves learning from labeled examples, unsupervised learning involves categorizing without labels, and reinforcement learning involves learning behaviors to achieve goals through interaction. The document also discusses regression vs classification problems, the learning and testing process, and examples of machine learning applications like customer profiling, face recognition, and handwritten character recognition.
Data mining is the process of automatically discovering useful information from large data sets. It draws from machine learning, statistics, and database systems to analyze data and identify patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk assessment, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.
The document describes the SPADE algorithm for sequential pattern mining. SPADE aims to improve efficiency over previous algorithms by reducing the number of database scans and using a more compact vertical id-list database format. It works by decomposing the search space into equivalence classes based on common prefixes and processing each class independently with depth-first search.
Association rule mining is used to find interesting relationships among data items in large datasets. It can help with business decision making by analyzing customer purchasing patterns. For example, market basket analysis looks at what items are frequently bought together. Association rules use support and confidence metrics, where support is the probability an itemset occurs and confidence is the probability that a rule is correct. The Apriori algorithm is commonly used to generate association rules by first finding frequent itemsets that meet a minimum support threshold across multiple passes of the data. It then generates rules from those itemsets if they meet a minimum confidence. Association rule mining has various applications and can provide useful insights but also has computational limitations.
Supervised learning and Unsupervised learning Usama Fayyaz
This document discusses supervised and unsupervised machine learning. Supervised learning uses labeled training data to learn a function that maps inputs to outputs. Unsupervised learning is used when only input data is available, with the goal of modeling underlying structures or distributions in the data. Common supervised algorithms include decision trees and logistic regression, while common unsupervised algorithms include k-means clustering and dimensionality reduction.
The document provides an overview of data mining concepts including association rules, classification, and clustering algorithms. It introduces data mining and knowledge discovery processes. Association rule mining aims to find relationships between variables in large datasets using the Apriori and FP-growth algorithms. Classification algorithms build a model to predict class membership for new records based on a decision tree. Clustering algorithms group similar records together without predefined classes.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Web mining uses data mining techniques to extract information from web documents and services. It involves web content mining of page content and search results, web structure mining of hyperlink structures, and web usage mining of server logs to find user access patterns. Data mining techniques like classification, clustering, and association rule mining can be applied to web data to discover useful patterns and information.
Association Rule Learning Part 1: Frequent Itemset GenerationKnoldus Inc.
A methodology useful for discovering interesting relationships hidden in large data sets. The uncovered relationships can be presented in the form of association rules.
This slide first introduces the sequential pattern mining problem and also presents some required definitions in order to understand GSP algorithm. At then end there is a brief introduction of GSP algorithm and some practical constraints which it supports.
This document discusses unsupervised machine learning classification through clustering. It defines clustering as the process of grouping similar items together, with high intra-cluster similarity and low inter-cluster similarity. The document outlines common clustering algorithms like K-means and hierarchical clustering, and describes how K-means works by assigning points to centroids and iteratively updating centroids. It also discusses applications of clustering in domains like marketing, astronomy, genomics and more.
The comparative study of apriori and FP-growth algorithmdeepti92pawar
This document summarizes a seminar presentation comparing the Apriori and FP-Growth algorithms for association rule mining. The document introduces association rule mining and frequent itemset mining. It then describes the Apriori algorithm, including its generate-and-test approach and bottlenecks. Next, it explains the FP-Growth algorithm, including how it builds an FP-tree to efficiently extract frequent itemsets without candidate generation. Finally, it provides results comparing the performance of the two algorithms and concludes that FP-Growth is more efficient for mining long patterns.
This document discusses association rule mining, which aims to find rules that predict the occurrence of items based on other items in transactions. It defines key concepts like frequent itemsets, support, confidence and association rules. It describes the Apriori algorithm for efficiently mining frequent itemsets by leveraging the anti-monotone property of support. The document also discusses challenges in applying association rule mining to categorical and continuous attributes in record data.
This document discusses association rule mining. It begins by defining the task of association rule mining as finding rules that predict the occurrence of items based on other items in transactions. It then describes how association rules are generated in two steps: first finding frequent itemsets whose support is above a minimum threshold, and then generating rules from those itemsets. The key challenge is that a brute force approach is computationally prohibitive due to the huge number of possible rules, so techniques like the Apriori algorithm are used to prune the search space.
The document discusses association rule mining. It defines frequent itemsets as itemsets whose support is greater than or equal to a minimum support threshold. Association rules are implications of the form X → Y, where X and Y are disjoint itemsets. Support and confidence are used to evaluate rules. The Apriori algorithm is introduced as a two-step approach to generate frequent itemsets and rules by pruning the search space using an anti-monotonic property of support.
The document discusses data mining techniques for association rule mining. It defines association rules as relationships of the form A implies B, where A and B are itemsets in transactional data. The key steps are finding frequent itemsets that meet a minimum support threshold, and generating association rules from those itemsets that meet a minimum confidence threshold. The Apriori algorithm is described as the most common approach for efficiently finding all frequent itemsets in an iterative way by pruning itemsets with subsets that are not frequent.
Lecture 4: Frequent Itemests, Association Rules. Evaluation. Beyond Apriori (ppt, pdf)
Chapter 6 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.
Chapter 6 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman.
Apriori and Eclat algorithm in Association Rule MiningWan Aezwani Wab
The document summarizes lecture 4 of a data warehousing and mining course. It discusses association rule mining, which aims to find relationships between items in transaction data. It defines key concepts like frequent itemsets, support, confidence and association rules. It also describes algorithms like Apriori and FP-Growth for efficiently mining frequent itemsets and generating association rules from transaction data.
MODULE 5 _ Mining frequent patterns and associations.pptxnikshaikh786
1. The FP-Growth algorithm constructs an FP-tree to store transaction data, with frequent items listed in descending order of frequency.
2. It then uses a divide-and-conquer strategy to mine the conditional pattern base of each frequent item prefix, extracting combinations of frequent items.
3. This recursively mines the frequent patterns from the conditional FP-tree for each prefix path, without generating a large number of candidate itemsets.
This document discusses various techniques for association rule mining, including frequent pattern mining. It defines key concepts like support, confidence, frequent itemsets, and association rules. It describes the Apriori algorithm for mining frequent itemsets and generating association rules. It also introduces FP-Growth as an alternative approach that avoids candidate generation through the use of an FP-tree to compress the transaction database. The document provides examples to illustrate frequent and maximal frequent itemsets.
ASSOCIATION Rule plus MArket basket Analysis.pptxSherishJaved
This document provides an introduction to association rule mining. It discusses how association rules are used to find frequent patterns and correlations among items in transactional databases. The document outlines the Apriori algorithm, which is the most influential algorithm for mining association rules. Apriori works in two steps: (1) finding all frequent itemsets whose support is above a minimum threshold, and (2) generating association rules from the frequent itemsets that have confidence above a minimum. An example of applying Apriori to a sample transactional database is provided to illustrate the algorithm.
This document outlines the process of association rule mining using the Apriori algorithm. It begins with definitions of key terms like frequent itemsets, support, and confidence. It then explains how the Apriori algorithm reduces the search space using the Apriori property to only consider potentially frequent itemsets. Finally, it provides examples applying the Apriori algorithm to transaction databases to generate strong association rules that meet minimum support and confidence thresholds.
This document discusses association rule learning and frequent pattern mining. It begins with an introduction to association rule mining using a grocery store example. It then describes the Apriori algorithm for finding frequent itemsets and generating association rules. The algorithm works in two steps - first finding all frequent itemsets whose support is above a minimum threshold, and then generating association rules from those itemsets where the confidence is above a minimum. An example run of the Apriori algorithm on a transactional database is shown. Finally, some potential application areas for association rule mining are discussed.
Mining Frequent Patterns, Association and CorrelationsJustin Cletus
This document summarizes Chapter 6 of the book "Data Mining: Concepts and Techniques" which discusses frequent pattern mining. It introduces basic concepts like frequent itemsets and association rules. It then describes several scalable algorithms for mining frequent itemsets, including Apriori, FP-Growth, and ECLAT. It also discusses optimizations to Apriori like partitioning the database and techniques to reduce the number of candidates and database scans.
The document summarizes a seminar presentation comparing the Apriori and FP-Growth algorithms for association rule mining. It introduces association rule mining and definitions like frequent itemsets and association rules. It then provides details on the Apriori algorithm, which uses a generate-and-test methodology with a join step to generate candidate itemsets and a prune step to determine frequent itemsets. The FP-Growth algorithm is also discussed but with less detail. The presentation aims to compare the performance of these two common association rule mining algorithms.
The document summarizes the key concepts and algorithms for mining frequent patterns and association rules from transactional databases. It introduces the Apriori algorithm, which uses a candidate generation-and-test approach to find frequent itemsets. It then describes the FP-Growth algorithm, which avoids candidate generation by constructing an FP-tree to directly mine frequent patterns without multiple database scans.
Lecture 3: Frequent Itemsets, Association Rules, Apriori algorithm.(ppt, pdf)
Chapter 6 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.
Chapter 6 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman.
This chapter discusses frequent pattern mining, which involves finding patterns that frequently occur in transactional or other forms of data. It covers basic concepts like frequent itemsets and association rules. It also describes several algorithms for efficiently mining frequent patterns at scale, including Apriori, FP-Growth, and the ECLAT algorithm. These algorithms aim to address the computational challenges of candidate generation and database scanning.
This presentation summarizes association rule mining and the Apriori algorithm. It defines association rules as being of the form X->Y, where X and Y are disjoint itemsets. The strength of a rule is determined by its support and confidence. The Apriori algorithm uses a level-wise approach to generate frequent itemsets and association rules from transaction data, starting with individual items and building up itemsets of increasing size as long as they meet a minimum support threshold. It pioneered the use of support-based pruning to efficiently mine frequent itemsets and rules from large datasets.
The document discusses mining association rules from transactional databases. It describes how association rule mining aims to find frequent patterns, associations, correlations or causal structures among items in transactional data. The key concepts of support and confidence for determining interesting association rules are introduced. The document outlines the Apriori algorithm for mining frequent itemsets, including the concepts of candidate generation, pruning and multiple database scans. Methods for improving the efficiency of Apriori, such as candidate pruning and partitioning, are also discussed. Finally, an alternative approach of using frequent-pattern trees to avoid costly candidate generation is presented.
Chapter 6. Mining Frequent Patterns, Associations and Correlations Basic Conc...Subrata Kumer Paul
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
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Search engines allow users to search the vast collection of documents on the web. They consist of crawlers that fetch web pages, indexers that analyze page content and links, and interfaces that allow users to enter queries. Crawlers add pages to an index by following links, and indexers create inverted indexes to map words to pages. When a query is searched, results are retrieved from the index and ranked based on relevance. PageRank is a key algorithm that ranks pages higher that receive more links from other highly ranked pages. While it effectively searches the large, diverse and dynamic web, search poses challenges in understanding ambiguous queries over an evolving collection.
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The document discusses information privacy and data mining. It defines information privacy as an individual's ability to control how information about them is shared. It outlines the basic OECD principles for protecting information privacy, including collection limitation, purpose specification, use limitation, security safeguards, and accountability. It describes common uses of data mining like fraud prevention but also potential misuses that can violate privacy. The document also discusses the primary aims of data mining applications and five pitfalls like unintentional mistakes, intentional abuse, and mission creep.
The document discusses cluster analysis, which groups data objects into clusters so that objects within a cluster are similar but dissimilar to objects in other clusters. It describes key characteristics of clustering, including that it is unsupervised learning and the clusters are determined algorithmically rather than by humans. Various clustering algorithms are covered, including partitioning, hierarchical, density-based, and grid-based methods. Applications of clustering discussed include business intelligence, image recognition, web search, outlier detection, and biology. Requirements for effective clustering in data mining are also outlined.
Classification techniques in data miningKamal Acharya
The document discusses classification algorithms in machine learning. It provides an overview of various classification algorithms including decision tree classifiers, rule-based classifiers, nearest neighbor classifiers, Bayesian classifiers, and artificial neural network classifiers. It then describes the supervised learning process for classification, which involves using a training set to construct a classification model and then applying the model to a test set to classify new data. Finally, it provides a detailed example of how a decision tree classifier is constructed from a training dataset and how it can be used to classify data in the test set.
This document outlines a chapter on data preprocessing that discusses data types, attributes, and preprocessing tasks. It begins by defining data and attributes, then describes different types of attributes like nominal, binary, ordinal, and numeric attributes. It also discusses different types of datasets like records, documents, transactions, and graphs. The major section on data preprocessing outlines why it is important and describes tasks like data cleaning, integration, transformation, reduction, and discretization to prepare dirty or unstructured data for analysis.
Introduction to Data Mining and Data WarehousingKamal Acharya
This document provides details about a course on data mining and data warehousing. The course objectives are to understand the foundational principles and techniques of data mining and data warehousing. The course description covers topics like data preprocessing, classification, association analysis, cluster analysis, and data warehouses. The course is divided into 10 units that cover concepts and algorithms for data mining techniques. Practical exercises are included to apply techniques to real-world data problems.
How to Download & Install Module From the Odoo App Store in Odoo 17Celine George
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UDL has gained in popularity over the last decade both in the K-12 and the post-secondary sectors. The usefulness of UDL to create inclusive learning experiences for the full array of diverse learners has been well documented in the literature, and there is now increasing scholarship examining the process of integrating UDL strategically across organisations. One concern, however, remains under-reported and under-researched. Much of the scholarship on UDL ironically remains while and Eurocentric. Even if UDL, as a discourse, considers the decolonization of the curriculum, it is abundantly clear that the research and advocacy related to UDL originates almost exclusively from the Global North and from a Euro-Caucasian authorship. It is argued that it is high time for the way UDL has been monopolized by Global North scholars and practitioners to be challenged. Voices discussing and framing UDL, from the Global South and Indigenous communities, must be amplified and showcased in order to rectify this glaring imbalance and contradiction.
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Keynote presentation to the Educational Leaders hui Kōkiritia Marautanga held in Auckland on 26 June 2024. Provides a high level overview of the history and development of the science of learning, and implications for the design of learning in our modern schools and classrooms.
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- students will be able to identify and name various types of ornamental plants commonly used in landscaping and decoration, classifying them based on their characteristics such as foliage, flowering, and growth habits. They will understand the ecological, aesthetic, and economic benefits of ornamental plants, including their roles in improving air quality, providing habitats for wildlife, and enhancing the visual appeal of environments. Additionally, students will demonstrate knowledge of the basic requirements for growing ornamental plants, ensuring they can effectively cultivate and maintain these plants in various settings.
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2. Compiled by: Kamal Acharya
Association Analysis
• Association rules analysis is a technique to uncover(mine) how
items are associated to each other.
• Such uncovered association between items are called association
rules
• When to mine association rules?
– Scenario:
• You are a sales manager
• Customer bought a pc and a digital camera recently
• What should you recommend to her next?
• Association rules are helpful In making your recommendation.
3. Compiled by: Kamal Acharya
Contd…
• Frequent patterns(item sets):
– Frequent patterns are patterns that appear frequently in a data
set.
– E.g.,
• In transaction data set milk and bread is a frequent pattern,
• In a shopping history database first buy pc, then a digital camera, and
then a memory card is another example of frequent pattern.
– Finding frequent patterns plays an essential role in mining
association rules.
4. Compiled by: Kamal Acharya
Frequent pattern mining
• Frequent pattern mining searches for recurring relationships in a given
data set.
• Frequent pattern mining for the discovery of interesting associations
between item sets
• Such associations can be applicable in many business decision making
processes such as:
– Catalog design
– Basket data analysis
– cross-marketing,
– sale campaign analysis,
– Web log (click stream) analysis, etc
5. Compiled by: Kamal Acharya
Market Basket analysis
A typical example of frequent pattern(item set) mining for association rules.
• Market basket analysis analyzes customer buying habits by finding
associations between the different items that customers place in their shopping
baskets.
Applications: To make marketing strategies
Example of Association Rule:
milk bread
6. Definition: Frequent Itemset
• Itemset
– A collection of one or more items
• Example: {Milk, Bread, Diaper}
– k-itemset
• An itemset that contains k items
• Support count ()
– Frequency of occurrence of an itemset
– E.g. ({Milk, Bread,Diaper}) = 2
• Support
– Fraction of transactions that contain an
itemset
– E.g. s({Milk, Bread, Diaper}) = 2/5
• Frequent Itemset
– An itemset whose support is greater than or
equal to a minsup threshold
TID Items
1 Bread, Milk
2 Bread, Diaper, Beer, Eggs
3 Milk, Diaper, Beer, Coke
4 Bread, Milk, Diaper, Beer
5 Bread, Milk, Diaper, Coke
Compiled by: Kamal Acharya
7. Definition: Association Rule
Association Rule :
An implication expression of the form X Y, where X and Y are itemsets
e.g.,
•It is not very difficult to develop algorithms that will find this
associations in a large database.
•The problem is that such an algorithm will also uncover many other
associations that are of very little value.
It is necessary to introduce some measures to distinguish
interesting associations from non-interesting ones.
Beer}Diaper,Milk{
Compiled by: Kamal Acharya
8. Definition: Association Rule
Example:
Beer}Diaper,Milk{
4.0
5
2
|T|
)BeerDiaper,,Milk(
s
67.0
3
2
)Diaper,Milk(
)BeerDiaper,Milk,(
c
Rule Evaluation Metrics
Support (s)
Fraction of transactions that
contain both X and Y
Alternatively, support, s,
probability that a
transaction contains {X Y}
Confidence (c)
Measures how often items in Y
appear in transactions that
contain X
Alternatively, confidence, c,
conditional probability that a
transaction having X also contains
Y
TID Items
1 Bread, Milk
2 Bread, Diaper, Beer, Eggs
3 Milk, Diaper, Beer, Coke
4 Bread, Milk, Diaper, Beer
5 Bread, Milk, Diaper, Coke
Compiled by: Kamal Acharya
9. TID date items_bought
100 10/10/99 {F,A,D,B}
200 15/10/99 {D,A,C,E,B}
300 19/10/99 {C,A,B,E}
400 20/10/99 {B,A,D}
Example1
• What is the support and confidence of the rule: {B,D} {A}
Support:
percentage of tuples that contain {A,B,D} =
Confidence:
D}{B,containthattuplesofnumber
D}B,{A,containthattuplesofnumber
75%
100%
Compiled by: Kamal Acharya
10. Compiled by: Kamal Acharya
Example2
• Given data set D is:
• What is the support and confidence of the rule:
– A C
– C A
• For rule A C
– Support =50% and
– Confidence = 66.6%)
• For rule C A
– Support=50% and
– Confidence = 100%
Transaction ID Items Bought
2000 A,B,C
1000 A,C
4000 A,D
5000 B,E,F
11. Association Rule Mining Task
• Given a set of transactions T, the goal of association rule mining
is to find all rules having
– support ≥ minsup threshold
– confidence ≥ minconf threshold
• Brute-force approach:
– List all possible association rules
– Compute the support and confidence for each rule
– Prune rules that fail the minsup and minconf thresholds
Computationally prohibitive!
Compiled by: Kamal Acharya
12. Mining Association Rules
Example of Rules:
{Milk,Diaper} {Beer} (s=0.4, c=0.67)
{Milk,Beer} {Diaper} (s=0.4, c=1.0)
{Diaper,Beer} {Milk} (s=0.4, c=0.67)
{Beer} {Milk,Diaper} (s=0.4, c=0.67)
{Diaper} {Milk,Beer} (s=0.4, c=0.5)
{Milk} {Diaper,Beer} (s=0.4, c=0.5)
TID Items
1 Bread, Milk
2 Bread, Diaper, Beer, Eggs
3 Milk, Diaper, Beer, Coke
4 Bread, Milk, Diaper, Beer
5 Bread, Milk, Diaper, Coke
Observations:
• All the above rules are binary partitions of the same itemset:
{Milk, Diaper, Beer}
• Rules originating from the same itemset have identical support but
can have different confidence
• Thus, we may decouple the support and confidence requirements
Compiled by: Kamal Acharya
13. Mining Association Rules
• Two-step approach:
1. Frequent Itemset Generation
– Generate all itemsets whose support minsup
2. Rule Generation
– Generate high confidence rules from each frequent itemset,
where each rule is a binary partitioning of a frequent
itemset
• Frequent itemset generation is still computationally
expensive!
Compiled by: Kamal Acharya
14. Frequent Itemset Generation
null
AB AC AD AE BC BD BE CD CE DE
A B C D E
ABC ABD ABE ACD ACE ADE BCD BCE BDE CDE
ABCD ABCE ABDE ACDE BCDE
ABCDE
Given d items, there
are 2d possible
candidate itemsets
Compiled by: Kamal Acharya
15. Reducing Number of Candidates
• Apriori principle:
– If an itemset is frequent, then all of its subsets must also be
frequent
– if {beer, diaper, nuts} is frequent, so is {beer, diaper}
• Apriori principle holds due to the following property of the
support measure:
– Support of an itemset never exceeds the support of its subsets
)()()(:, YsXsYXYX
Compiled by: Kamal Acharya
17. Compiled by: Kamal Acharya
Contd…
• How is the apriori property is used in the algorithm?
– Apriori pruning principle: If there is any itemset which is infrequent, its
superset should not be generated/tested!
Found to be
Infrequent
null
AB AC AD AE BC BD BE CD CE DE
A B C D E
ABC ABD ABE ACD ACE ADE BCD BCE BDE CDE
ABCD ABCE ABDE ACDE BCDE
ABCDE
null
AB AC AD AE BC BD BE CD CE DE
A B C D E
ABC ABD ABE ACD ACE ADE BCD BCE BDE CDE
ABCD ABCE ABDE ACDE BCDE
ABCDE
Pruned
supersets
18. Illustrating Apriori purning Principle
Item Count
Bread 4
Coke 2
Milk 4
Beer 3
Diaper 4
Eggs 1
Itemset Count
{Bread,Milk} 3
{Bread,Beer} 2
{Bread,Diaper} 3
{Milk,Beer} 2
{Milk,Diaper} 3
{Beer,Diaper} 3
Items (1-itemsets)
Pairs (2-itemsets)
(No need to generate
candidates involving Coke
or Eggs)
Minimum Support = 3
Compiled by: Kamal Acharya
19. The Apriori Algorithm (the general idea)
1. Initially, scan DB once to get candidate item set C1 and find frequent
1-items from C1and put them to Lk (k=1)
2. Use Lk to generate a collection of candidate itemsets Ck+1 with size
(k+1)
3. Scan the database to find which itemsets in Ck+1 are frequent and put
them into Lk+1
4. If Lk+1 is not empty(i.e., terminate when no frequent or candidate set
can be generated)
k=k+1
GOTO 2
Compiled by: Kamal Acharya
20. Compiled by: Kamal Acharya
Generating association rules from frequent itemsets
• generate strong association rules from frequent itemsets (where strong
association rules satisfy both minimum support and minimum confidence) as
follows:
• The rules generated from frequent itemsets, each one automatically satisfies
the minimum support.
21. Compiled by: Kamal Acharya
The Apriori Algorithm — Example1
• Consider the following transactions for association rules analysis:
• Use minimum support(min_sup) = 2 (2/9 = 22%) and
• Minimum confidence = 70%
23. Compiled by: Kamal Acharya
Contd…
• Step2: Generating association rules: The data contain frequent
itemset X ={I1,I2,I5} .What are the association rules that can be generated
from X?
• The nonempty subsets of X are . The
resulting association rules are as shown below, each listed with its confidence:
• Here, minimum confidence threshold is 70%, so only the second, third, and
last rules are output, because these are the only ones generated that are strong.
26. Compiled by: Kamal Acharya
Homework
• A database has five transactions. Let min sup = 60% and min
conf = 80%.
• Find all frequent itemsets using Apriori algorithm.
• List all the strong association rules.
27. Problems with A-priori Algorithms
• It is costly to handle a huge number of candidate sets.
– For example if there are 104 large 1-itemsets, the Apriori algorithm will need to
generate more than 107 candidate 2-itemsets. Moreover for 100-itemsets, it must
generate more than 2100 1030 candidates in total.
• The candidate generation is the inherent cost of the Apriori
Algorithms, no matter what implementation technique is applied.
• To mine a large data sets for long patterns – this algorithm is
NOT a good idea.
• When Database is scanned to check Ck for creating Lk, a large
number of transactions will be scanned even they do not contain
any k-itemset.
Compiled by: Kamal Acharya
28. Frequent pattern growth (FP-growth):
• A frequent pattern growth approach mines Frequent Patterns
Without Candidate Generation.
• In FP-Growth there are mainly two step involved:
– Build a compact data structure called FP-Tree and
– Than, extract frequent itemset directly from the FP-tree.
Compiled by: Kamal Acharya
29. Compiled by: Kamal Acharya
FP-tree Construction from a Transactional DB
• FP-Tree is constructed using two passes over the data set:
• Pass1:
1. Scan DB once, find frequent 1-itemsets (single item patterns)
1. Scan DB and find support for each item.
2. Discard infrequent items
2. sort frequent items in descending order of their frequency(support
count).
3. Sort the items in each transaction in descending order of their
frequency.
Use this order when building the FP-tree, so common prefixes can be
shared.
• Pass2: Scan DB again, construct FP-tree
1. FP-growth reads one transaction at a time and maps it to a path.
2. Fixed order is used, so path can overlap when transaction share items.
3. Pointers are maintained between nodes containing the same item(doted
line)
30. Mining Frequent Patterns Using FP-tree
• The FP-tree is mined as follows:
– Start from each frequent length-1 pattern (as an initial suffix
pattern)
– construct its conditional pattern base (a “sub-database,” which
consists of the set of prefix paths in the FP-tree co-occurring with
the suffix pattern)
– then construct its (conditional) FP-tree, and perform mining
recursively on such a tree.
– The pattern growth is achieved by the concatenation of the suffix
pattern with the frequent patterns generated from a conditional FP-
tree.
Compiled by: Kamal Acharya
31. Compiled by: Kamal Acharya
FP-tree Construction
• Let transaction database D:
• Let min_support = 3
32. Compiled by: Kamal Acharya
FP-tree Construction
• Pass 1: Finding frequent 1-itemset and sorting the this set in descending order of
support count(frequency):
• Then, Making sorted frequent transactions in the transaction dataset D:
Item frequency
f 4
c 4
a 3
b 3
m 3
p 3
TID Items bought (ordered) frequent items
100 {f, a, c, d, g, i, m, p} {f, c, a, m, p}
200 {a, b, c, f, l, m, o} {f, c, a, b, m}
300 {b, f, h, j, o} {f, b}
400 {b, c, k, s, p} {c, b, p}
500 {a, f, c, e, l, p, m, n} {f, c, a, m, p}
33. FP-tree Construction
root
TID freq. Items bought
100 {f, c, a, m, p}
200 {f, c, a, b, m}
300 {f, b}
400 {c, p, b}
500 {f, c, a, m, p}
Item frequency
f 4
c 4
a 3
b 3
m 3
p 3
min_support = 3
f:1
c:1
a:1
m:1
p:1
Compiled by: Kamal Acharya
34. FP-tree Construction
root
Item frequency
f 4
c 4
a 3
b 3
m 3
p 3
min_support = 3
f:2
c:2
a:2
m:1
p:1
b:1
m:1
TID freq. Items bought
100 {f, c, a, m, p}
200 {f, c, a, b, m}
300 {f, b}
400 {c, p, b}
500 {f, c, a, m, p}
Compiled by: Kamal Acharya
35. FP-tree Construction
root
Item frequency
f 4
c 4
a 3
b 3
m 3
p 3
min_support = 3
f:3
c:2
a:2
m:1
p:1
b:1
m:1
b:1
TID freq. Items bought
100 {f, c, a, m, p}
200 {f, c, a, b, m}
300 {f, b}
400 {c, p, b}
500 {f, c, a, m, p}
Compiled by: Kamal Acharya
36. FP-tree Construction
root
Item frequency
f 4
c 4
a 3
b 3
m 3
p 3
min_support = 3
f:3
c:2
a:2
m:1
p:1
b:1
m:1
b:1
TID freq. Items bought
100 {f, c, a, m, p}
200 {f, c, a, b, m}
300 {f, b}
400 {c, p, b}
500 {f, c, a, m, p}
c:1
b:1
p:1
Compiled by: Kamal Acharya
37. FP-tree Construction
root
Item frequency
f 4
c 4
a 3
b 3
m 3
p 3
min_support = 3
f:4
c:3
a:3
m:2
p:2
b:1
m:1
b:1
TID freq. Items bought
100 {f, c, a, m, p}
200 {f, c, a, b, m}
300 {f, b}
400 {c, p, b}
500 {f, c, a, m, p}
c:1
b:1
p:1
Header Table
Item frequency head
f 4
c 4
a 3
b 3
m 3
p 3
Compiled by: Kamal Acharya
38. Benefits of the FP-tree Structure
• Completeness:
– never breaks a long pattern of any transaction
– preserves complete information for frequent pattern mining
• Compactness
– reduce irrelevant information—infrequent items are gone
– frequency descending ordering: more frequent items are more likely to be
shared
– never be larger than the original database.
Compiled by: Kamal Acharya
39. Mining Frequent Patterns Using FP-tree
• The FP-tree is mined as follows:
– Start from each frequent length-1 pattern (as an initial suffix
pattern)
– construct its conditional pattern base (a “sub-database,” which
consists of the set of prefix paths in the FP-tree co-occurring with
the suffix pattern)
– then construct its (conditional) FP-tree, and perform mining
recursively on such a tree.
– The pattern growth is achieved by the concatenation of the suffix
pattern with the frequent patterns generated from a conditional FP-
tree.
Compiled by: Kamal Acharya
40. Mining Frequent Patterns Using the FP-tree
(cont’d)
• Start with last item in order (i.e., p).
• Follow node pointers and traverse only the paths containing p.
• Accumulate all of transformed prefix paths of that item to form a
conditional pattern base
Conditional pattern base for p
fcam:2, cb:1
f:4
c:3
a:3
m:2
p:2
c:1
b:1
p:1
p
Construct a new FP-tree based on this
pattern, by merging all paths and
keeping nodes that appear sup times.
This leads to only one branch c:3
Thus we derive only one frequent
pattern cont. p. Pattern cp
Compiled by: Kamal Acharya
41. Mining Frequent Patterns Using the FP-tree
(cont’d)
• Move to next least frequent item in order, i.e., m
• Follow node pointers and traverse only the paths containing m.
• Accumulate all of transformed prefix paths of that item to form a conditional
pattern base
f:4
c:3
a:3
m:2
m
m:1
b:1
m-conditional pattern base: fca:2, fcab:1
{}
f:3
c:3
a:3
m-conditional FP-tree (contains only path fca:3)
All frequent patterns that include m
m,
fm, cm, am,
fcm, fam, cam,
fcam
Compiled by: Kamal Acharya
42. Conditional Pattern-Bases for the example
EmptyEmptyf
{(f:3)}|c{(f:3)}c
{(f:3, c:3)}|a{(fc:3)}a
Empty{(fca:1), (f:1), (c:1)}b
{(f:3, c:3, a:3)}|m{(fca:2), (fcab:1)}m
{(c:3)}|p{(fcam:2), (cb:1)}p
Conditional FP-treeConditional pattern-baseItem
Compiled by: Kamal Acharya
43. Why Is Frequent Pattern Growth Fast?
• Performance studies show
– FP-growth is an faster than Apriori, and is also
• Reasoning
– No candidate generation, no candidate test
– Uses compact data structure
– Eliminates repeated database scan
– Basic operation is counting and FP-tree building
Compiled by: Kamal Acharya
44. Compiled by: Kamal Acharya
Handling Categorical Attributes
• So far, we have used only transaction data for mining association rules.
• The data can be in transaction form or table form
Transaction form: t1: a, b
t2: a, c, d, e
t3: a, d, f
Table form:
• Table data need to be converted to transaction form for association rule
mining
Attr1 Attr2 Attr3
a b d
b c e
45. Compiled by: Kamal Acharya
Contd…
• To convert a table data set to a transaction data set simply change
each value to an attribute–value pair.
• For example:
46. Compiled by: Kamal Acharya
Contd…
• Each attribute–value pair is considered an item.
• Using only values is not sufficient in the transaction form
because different attributes may have the same values.
• For example, without including attribute names, value a’s for
Attribute1 and Attribute2 are not distinguishable.
• After the conversion, Figure (B) can be used in mining.
47. Compiled by: Kamal Acharya
Home Work
• Convert the following categorical attribute data into transaction
data set
48. Compiled by: Kamal Acharya
Homework
• What is the aim of association rule mining? Why is this aim important in some
application?
• Define the concepts of support and confidence for an association rule.
• Show how the apriori algorithm works on an example dataset.
• What is the basis of the apriori algorithm? Describe the algorithm briefly.
Which step of the algorithm can become a bottleneck?
49. Compiled by: Kamal Acharya
Contd…
• Show using an example how FP-tree algorithm solves the association rule
mining (ARM) problem.
• Perform ARM using FP-growth on the following data set with minimum
support = 50% and confidence = 75%
Transaction ID Items
1 Bread, cheese, Eggs, Juice
2 Bread, Cheese, Juice
3 Bread, Milk, Yogurt
4 Bread, Juice, Milk
5 Cheese, Juice, Milk
雅虎竞拍
煤炉代买
paypay商城
乐天二手
日本亚马逊
乐天新品
ZOZOTOWN
