This document discusses data mining techniques including classification, clustering, regression, and association rules. It provides examples of how each technique works and areas where they are applied, such as marketing, risk assessment, fraud detection, and customer care. The advantages of data mining are that it provides new knowledge from existing data that can improve products, services and profits. However, privacy is a concern when linking multiple data sources to gain a wide range of information about individuals.
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 discover patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk analysis, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.
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 discusses association rule mining which aims to discover relationships between items in transactional data. It defines key concepts like support, confidence and association rules. It also describes several algorithms for mining association rules like Apriori, Partition and Pincer-Search. Apriori is a level-wise, candidate generation-based approach that leverages the downward closure property. Partition divides the database to mine local frequent itemsets in parallel. Pincer-Search incorporates bidirectional search to prune candidates more efficiently.
This document discusses data mining techniques, including the data mining process and common techniques like association rule mining. It describes the data mining process as involving data gathering, preparation, mining the data using algorithms, and analyzing and interpreting the results. Association rule mining is explained in detail, including how it can be used to identify relationships between frequently purchased products. Methods for mining multilevel and multidimensional association rules are also summarized.
The document provides an approach for improving the efficacy of alerts from anti-money laundering transaction monitoring models by reducing false positives. The approach involves regularly evaluating rule efficacy, acquiring historic transaction and disposition data, analyzing the data to understand patterns, building a detection engine to test threshold combinations, and quantitatively and qualitatively analyzing the results to identify the combination with the best efficacy based on case and SAR retention proportions while minimizing false positives. Key steps include prioritizing rules for tuning, testing threshold permutations, sampling transactions for investigator review, and approving final threshold changes. The goal is to generate higher quality alerts while controlling compliance costs.
Data mining is the process of analyzing large databases to discover useful patterns. It involves applying computer-based methods to derive knowledge from large amounts of data. The main components of data mining are knowledge discovery, where concrete information is gleaned from known data, and knowledge prediction, which uses known data to forecast future trends. Data is collected and stored in a centralized data warehouse to allow for easier querying. Common data mining techniques include classification, clustering, regression, and association rule mining. Data mining has various applications in areas such as business, science, medicine, and more to gain useful insights from data. However, effective data mining requires linking multiple data sources which can raise privacy concerns if a person's entire data history is assembled.
This document discusses data mining techniques including classification, clustering, regression, and association rules. It provides examples of how each technique works and areas where they are applied, such as marketing, risk assessment, fraud detection, and customer care. The advantages of data mining are that it provides new knowledge from existing data that can improve products, services and profits. However, privacy is a concern when linking multiple data sources to gain a wide range of information about individuals.
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 discover patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk analysis, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.
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 discusses association rule mining which aims to discover relationships between items in transactional data. It defines key concepts like support, confidence and association rules. It also describes several algorithms for mining association rules like Apriori, Partition and Pincer-Search. Apriori is a level-wise, candidate generation-based approach that leverages the downward closure property. Partition divides the database to mine local frequent itemsets in parallel. Pincer-Search incorporates bidirectional search to prune candidates more efficiently.
This document discusses data mining techniques, including the data mining process and common techniques like association rule mining. It describes the data mining process as involving data gathering, preparation, mining the data using algorithms, and analyzing and interpreting the results. Association rule mining is explained in detail, including how it can be used to identify relationships between frequently purchased products. Methods for mining multilevel and multidimensional association rules are also summarized.
The document provides an approach for improving the efficacy of alerts from anti-money laundering transaction monitoring models by reducing false positives. The approach involves regularly evaluating rule efficacy, acquiring historic transaction and disposition data, analyzing the data to understand patterns, building a detection engine to test threshold combinations, and quantitatively and qualitatively analyzing the results to identify the combination with the best efficacy based on case and SAR retention proportions while minimizing false positives. Key steps include prioritizing rules for tuning, testing threshold permutations, sampling transactions for investigator review, and approving final threshold changes. The goal is to generate higher quality alerts while controlling compliance costs.
Data mining is the process of analyzing large databases to discover useful patterns. It involves applying computer-based methods to derive knowledge from large amounts of data. The main components of data mining are knowledge discovery, where concrete information is gleaned from known data, and knowledge prediction, which uses known data to forecast future trends. Data is collected and stored in a centralized data warehouse to allow for easier querying. Common data mining techniques include classification, clustering, regression, and association rule mining. Data mining has various applications in areas such as business, science, medicine, and more to gain useful insights from data. However, effective data mining requires linking multiple data sources which can raise privacy concerns if a person's entire data history is assembled.
Reducing False Positives - BSA AML Transaction Monitoring Re-Tuning ApproachErik De Monte
This document outlines an approach for improving the efficacy of alerts from anti-money laundering transaction monitoring models. It involves regularly evaluating rule efficacy, acquiring historic transaction and alert data, analyzing the data to understand patterns, building a detection engine to test threshold combinations, running the data through the engine to calculate alert counts for each combination, analyzing combinations based on case and SAR retention, qualitatively evaluating samples, and selecting the optimal combination based on efficacy tests. The goal is to reduce false positives while still detecting true suspicious activity to improve compliance programs and control costs.
This document introduces the concept of association rule mining. Association rule mining aims to discover relationships between variables in large datasets. It analyzes how frequently items are purchased together by customers. This helps retailers understand customer purchasing habits and develop effective marketing strategies. The document defines key terms like transactions, itemsets, support count, and support. It distinguishes association rules from classification rules. Association rules show relationships between items rather than predicting class membership. The document uses examples from market basket analysis to illustrate association rule mining concepts.
The document discusses data mining primitives, languages, and system architecture. It defines data mining as extracting knowledge from large amounts of data and describes the typical components of a data mining system, including a user interface, data mining engine, database, and knowledge base. It then outlines several key data mining primitives, such as specifying the relevant data, type of knowledge to mine, background knowledge, and interestingness measures. Finally, it discusses languages for data mining, noting the challenges in designing one language to handle different mining tasks and describing some elements of the Data Mining Query Language (DMQL).
Classification on multi label dataset using rule mining techniqueeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document provides an overview of key concepts in data mining including data preprocessing, data warehouses, frequent patterns, association rule mining, classification, clustering, outlier analysis and more. It discusses different types of databases that can be mined such as relational, transactional, temporal and spatial databases. The document also covers data characterization, discrimination, interestingness measures and different types of data mining systems.
Computational Methods in Medicine
Angel Garrido
Faculty of Sciences, National University of Distance Education, Madrid, Spain
Paseo Senda del Rey, 9, 28040, Madrid, Spain
algbmv@telefonica.net
Abstract
Artificial Intelligence requires logic. But its classical version shows too many
insufficiencies. So, it is absolutely necessary to introduce more sophisticated tools, such as Fuzzy
Logic, Modal Logic, Non-Monotonic Logic, and so on [2]. Among the things that AI needs to
represent are categories, objects, properties, relations between objects, situations, states, time,
events, causes and effects, knowledge about knowledge, and so on. The problems in AI can be
classified in two general types [3, 4]: Search Problems and Representation Problem. There exist
different ways to reach this objective. So, we have [3] Logics, Rules, Frames, Associative Nets,
Scripts and so on, that are often interconnected. Also, it will be very useful, in dealing with
problems of uncertainty and causality, to introduce Bayesian Networks, and particularly, a principal
tool as the Essential Graph. We attempt here to show the scope of application of such versatile
methods, currently fundamental in Medicine.
Introduction To Multilevel Association Rule And Its MethodsIJSRD
Association rule mining is a popular and well researched method for discovering interesting relations between variables in large databases. In this paper we introduce the concept of Data mining, Association rule and Multilevel association rule with different algorithm, its advantage and concept of Fuzzy logic and Genetic Algorithm. Multilevel association rules can be mined efficiently using concept hierarchies under a support-confidence framework.
This document provides an introduction and overview of key concepts in data mining. It defines data mining as extracting hidden predictive information from large databases to help companies make knowledge-driven decisions. The document outlines different types of patterns that can be mined, including frequent patterns, associations, correlations, and outliers. It also discusses technologies commonly used in data mining such as statistics, machine learning, databases, and visualization. Major issues addressed include developing new mining methodologies, enabling user interaction, improving efficiency and scalability, handling diverse data types, and addressing societal impacts.
This document discusses various techniques for data preprocessing, including data cleaning, integration and transformation, reduction, and discretization. It provides details on techniques for handling missing data, noisy data, and data integration issues. It also describes methods for data transformation such as normalization, aggregation, and attribute construction. Finally, it outlines various data reduction techniques including cube aggregation, attribute selection, dimensionality reduction, and numerosity reduction.
The document discusses hierarchical database models and concepts including:
- Records are organized into a tree structure with parent-child relationships
- Queries can retrieve records by traversing the tree structure using pointers
- The IMS database system was an early commercial hierarchical database that used various access methods like HSAM and HISAM
The document discusses several topics related to advanced database applications including:
1. Active databases and triggers which allow specifying rules that automatically execute actions in response to events.
2. Temporal databases which incorporate time aspects into data organization and allow storing information about when events occur.
3. Multimedia databases which provide features for storing and querying different types of multimedia information like images, video, and audio.
Data preprocessing involves cleaning, transforming, and reducing raw data to improve its quality and prepare it for analysis. It addresses issues like missing values, noise, inconsistencies, and redundancies. The document outlines various techniques for data cleaning (e.g. handling missing values, smoothing noisy data), integration (e.g. schema matching), and reduction (e.g. dimensionality reduction, numerosity reduction). The goal of preprocessing is to produce quality data that leads to more accurate and efficient data mining and decision making.
Market basket analysis examines customer purchasing patterns to determine which items are commonly bought together. This can help retailers with marketing strategies like product bundling and complementary product placement. Association rule mining is a two-step process that first finds frequent item sets that occur together above a minimum support threshold, and then generates strong association rules from these frequent item sets that satisfy minimum support and confidence. Various techniques can improve the efficiency of the Apriori algorithm for mining association rules, such as hashing, transaction reduction, partitioning, sampling, and dynamic item-set counting. Pruning strategies like item merging, sub-item-set pruning, and item skipping can also enhance efficiency. Constraint-based mining allows users to specify constraints on the type of
Data Mining: Mining ,associations, and correlationsDatamining Tools
Market basket analysis examines customer purchasing patterns to determine which items are commonly bought together. This can help retailers with marketing strategies like product bundling and complementary product placement. Association rule mining is a two-step process that first finds frequent item sets that occur together above a minimum support threshold, and then generates strong association rules from these frequent item sets based on minimum support and confidence. Various techniques can improve the efficiency of the Apriori algorithm for mining association rules, such as hashing, transaction reduction, partitioning, sampling, and dynamic item-set counting. Pruning strategies like item merging, sub-item-set pruning, and item skipping can also enhance efficiency. Constraint-based mining allows users to specify constraints on the type of
Top Down Approach to find Maximal Frequent Item Sets using Subset Creationcscpconf
Association rule has been an area of active research in the field of knowledge discovery. Data
mining researchers had improved upon the quality of association rule mining for business
development by incorporating influential factors like value (utility), quantity of items sold
(weight) and more for the mining of association patterns. In this paper, we propose an efficient
approach to find maximal frequent item set first. Most of the algorithms in literature used to find
minimal frequent item first, then with the help of minimal frequent item sets derive the maximal
frequent item sets. These methods consume more time to find maximal frequent item sets. To
overcome this problem, we propose a navel approach to find maximal frequent item set directly using the concepts of subsets. The proposed method is found to be efficient in finding maximal frequent item sets.
The D-basis Algorithm for Association Rules of High ConfidenceITIIIndustries
We develop a new approach for distributed computing of the association rules of high confidence on the attributes/columns of a binary table. It is derived from the D-basis algorithm developed by K.Adaricheva and J.B.Nation (Theoretical Computer Science, 2017), which runs multiple times on sub-tables of a given binary table, obtained by removing one or more rows. The sets of rules retrieved at these runs are then aggregated. This allows us to obtain a basis of association rules of high confidence, which can be used for ranking all attributes of the table with respect to a given fixed attribute. This paper focuses on some algorithmic details and the technical implementation of the new algorithm. Results are given for tests performed on random, synthetic and real data
The document summarizes research on improving the Apriori algorithm for association rule mining. It first provides background on association rule mining and the standard Apriori algorithm. It then discusses several proposed improvements to Apriori, including reducing the number of database scans, shrinking the candidate itemset size, and using techniques like pruning and hash trees. Finally, it outlines some open challenges for further optimizing association rule mining.
This document summarizes research on improving the Apriori algorithm for mining association rules from transactional databases. It first provides background on association rule mining and describes the basic Apriori algorithm. The Apriori algorithm finds frequent itemsets by multiple passes over the database but has limitations of increased search space and computational costs as the database size increases. The document then reviews research on variations of the Apriori algorithm that aim to reduce the number of database scans, shrink the candidate sets, and facilitate support counting to improve performance.
This document discusses rotor machines, which are electro-mechanical stream cipher devices used to encrypt and decrypt secret messages. Rotor machines were widely used for cryptography from the 1920s to 1970s. The most famous example is the German Enigma machine, whose messages were deciphered by the Allies during World War II to produce intelligence code-named Ultra. The document also briefly mentions that substitution ciphers encrypt by replacing plaintext units like single letters or pairs of letters with ciphertext units.
Reducing False Positives - BSA AML Transaction Monitoring Re-Tuning ApproachErik De Monte
This document outlines an approach for improving the efficacy of alerts from anti-money laundering transaction monitoring models. It involves regularly evaluating rule efficacy, acquiring historic transaction and alert data, analyzing the data to understand patterns, building a detection engine to test threshold combinations, running the data through the engine to calculate alert counts for each combination, analyzing combinations based on case and SAR retention, qualitatively evaluating samples, and selecting the optimal combination based on efficacy tests. The goal is to reduce false positives while still detecting true suspicious activity to improve compliance programs and control costs.
This document introduces the concept of association rule mining. Association rule mining aims to discover relationships between variables in large datasets. It analyzes how frequently items are purchased together by customers. This helps retailers understand customer purchasing habits and develop effective marketing strategies. The document defines key terms like transactions, itemsets, support count, and support. It distinguishes association rules from classification rules. Association rules show relationships between items rather than predicting class membership. The document uses examples from market basket analysis to illustrate association rule mining concepts.
The document discusses data mining primitives, languages, and system architecture. It defines data mining as extracting knowledge from large amounts of data and describes the typical components of a data mining system, including a user interface, data mining engine, database, and knowledge base. It then outlines several key data mining primitives, such as specifying the relevant data, type of knowledge to mine, background knowledge, and interestingness measures. Finally, it discusses languages for data mining, noting the challenges in designing one language to handle different mining tasks and describing some elements of the Data Mining Query Language (DMQL).
Classification on multi label dataset using rule mining techniqueeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document provides an overview of key concepts in data mining including data preprocessing, data warehouses, frequent patterns, association rule mining, classification, clustering, outlier analysis and more. It discusses different types of databases that can be mined such as relational, transactional, temporal and spatial databases. The document also covers data characterization, discrimination, interestingness measures and different types of data mining systems.
Computational Methods in Medicine
Angel Garrido
Faculty of Sciences, National University of Distance Education, Madrid, Spain
Paseo Senda del Rey, 9, 28040, Madrid, Spain
algbmv@telefonica.net
Abstract
Artificial Intelligence requires logic. But its classical version shows too many
insufficiencies. So, it is absolutely necessary to introduce more sophisticated tools, such as Fuzzy
Logic, Modal Logic, Non-Monotonic Logic, and so on [2]. Among the things that AI needs to
represent are categories, objects, properties, relations between objects, situations, states, time,
events, causes and effects, knowledge about knowledge, and so on. The problems in AI can be
classified in two general types [3, 4]: Search Problems and Representation Problem. There exist
different ways to reach this objective. So, we have [3] Logics, Rules, Frames, Associative Nets,
Scripts and so on, that are often interconnected. Also, it will be very useful, in dealing with
problems of uncertainty and causality, to introduce Bayesian Networks, and particularly, a principal
tool as the Essential Graph. We attempt here to show the scope of application of such versatile
methods, currently fundamental in Medicine.
Introduction To Multilevel Association Rule And Its MethodsIJSRD
Association rule mining is a popular and well researched method for discovering interesting relations between variables in large databases. In this paper we introduce the concept of Data mining, Association rule and Multilevel association rule with different algorithm, its advantage and concept of Fuzzy logic and Genetic Algorithm. Multilevel association rules can be mined efficiently using concept hierarchies under a support-confidence framework.
This document provides an introduction and overview of key concepts in data mining. It defines data mining as extracting hidden predictive information from large databases to help companies make knowledge-driven decisions. The document outlines different types of patterns that can be mined, including frequent patterns, associations, correlations, and outliers. It also discusses technologies commonly used in data mining such as statistics, machine learning, databases, and visualization. Major issues addressed include developing new mining methodologies, enabling user interaction, improving efficiency and scalability, handling diverse data types, and addressing societal impacts.
This document discusses various techniques for data preprocessing, including data cleaning, integration and transformation, reduction, and discretization. It provides details on techniques for handling missing data, noisy data, and data integration issues. It also describes methods for data transformation such as normalization, aggregation, and attribute construction. Finally, it outlines various data reduction techniques including cube aggregation, attribute selection, dimensionality reduction, and numerosity reduction.
The document discusses hierarchical database models and concepts including:
- Records are organized into a tree structure with parent-child relationships
- Queries can retrieve records by traversing the tree structure using pointers
- The IMS database system was an early commercial hierarchical database that used various access methods like HSAM and HISAM
The document discusses several topics related to advanced database applications including:
1. Active databases and triggers which allow specifying rules that automatically execute actions in response to events.
2. Temporal databases which incorporate time aspects into data organization and allow storing information about when events occur.
3. Multimedia databases which provide features for storing and querying different types of multimedia information like images, video, and audio.
Data preprocessing involves cleaning, transforming, and reducing raw data to improve its quality and prepare it for analysis. It addresses issues like missing values, noise, inconsistencies, and redundancies. The document outlines various techniques for data cleaning (e.g. handling missing values, smoothing noisy data), integration (e.g. schema matching), and reduction (e.g. dimensionality reduction, numerosity reduction). The goal of preprocessing is to produce quality data that leads to more accurate and efficient data mining and decision making.
Market basket analysis examines customer purchasing patterns to determine which items are commonly bought together. This can help retailers with marketing strategies like product bundling and complementary product placement. Association rule mining is a two-step process that first finds frequent item sets that occur together above a minimum support threshold, and then generates strong association rules from these frequent item sets that satisfy minimum support and confidence. Various techniques can improve the efficiency of the Apriori algorithm for mining association rules, such as hashing, transaction reduction, partitioning, sampling, and dynamic item-set counting. Pruning strategies like item merging, sub-item-set pruning, and item skipping can also enhance efficiency. Constraint-based mining allows users to specify constraints on the type of
Data Mining: Mining ,associations, and correlationsDatamining Tools
Market basket analysis examines customer purchasing patterns to determine which items are commonly bought together. This can help retailers with marketing strategies like product bundling and complementary product placement. Association rule mining is a two-step process that first finds frequent item sets that occur together above a minimum support threshold, and then generates strong association rules from these frequent item sets based on minimum support and confidence. Various techniques can improve the efficiency of the Apriori algorithm for mining association rules, such as hashing, transaction reduction, partitioning, sampling, and dynamic item-set counting. Pruning strategies like item merging, sub-item-set pruning, and item skipping can also enhance efficiency. Constraint-based mining allows users to specify constraints on the type of
Top Down Approach to find Maximal Frequent Item Sets using Subset Creationcscpconf
Association rule has been an area of active research in the field of knowledge discovery. Data
mining researchers had improved upon the quality of association rule mining for business
development by incorporating influential factors like value (utility), quantity of items sold
(weight) and more for the mining of association patterns. In this paper, we propose an efficient
approach to find maximal frequent item set first. Most of the algorithms in literature used to find
minimal frequent item first, then with the help of minimal frequent item sets derive the maximal
frequent item sets. These methods consume more time to find maximal frequent item sets. To
overcome this problem, we propose a navel approach to find maximal frequent item set directly using the concepts of subsets. The proposed method is found to be efficient in finding maximal frequent item sets.
The D-basis Algorithm for Association Rules of High ConfidenceITIIIndustries
We develop a new approach for distributed computing of the association rules of high confidence on the attributes/columns of a binary table. It is derived from the D-basis algorithm developed by K.Adaricheva and J.B.Nation (Theoretical Computer Science, 2017), which runs multiple times on sub-tables of a given binary table, obtained by removing one or more rows. The sets of rules retrieved at these runs are then aggregated. This allows us to obtain a basis of association rules of high confidence, which can be used for ranking all attributes of the table with respect to a given fixed attribute. This paper focuses on some algorithmic details and the technical implementation of the new algorithm. Results are given for tests performed on random, synthetic and real data
The document summarizes research on improving the Apriori algorithm for association rule mining. It first provides background on association rule mining and the standard Apriori algorithm. It then discusses several proposed improvements to Apriori, including reducing the number of database scans, shrinking the candidate itemset size, and using techniques like pruning and hash trees. Finally, it outlines some open challenges for further optimizing association rule mining.
This document summarizes research on improving the Apriori algorithm for mining association rules from transactional databases. It first provides background on association rule mining and describes the basic Apriori algorithm. The Apriori algorithm finds frequent itemsets by multiple passes over the database but has limitations of increased search space and computational costs as the database size increases. The document then reviews research on variations of the Apriori algorithm that aim to reduce the number of database scans, shrink the candidate sets, and facilitate support counting to improve performance.
This document discusses rotor machines, which are electro-mechanical stream cipher devices used to encrypt and decrypt secret messages. Rotor machines were widely used for cryptography from the 1920s to 1970s. The most famous example is the German Enigma machine, whose messages were deciphered by the Allies during World War II to produce intelligence code-named Ultra. The document also briefly mentions that substitution ciphers encrypt by replacing plaintext units like single letters or pairs of letters with ciphertext units.
DVI,FRACTAL IMAGE,SUB BAND IMAGE,VIDEO CODING AND WAVELET BASED COMPRESSIONkirupasuchi1996
It will gives the exact definition about the Dvi,Fractal Image,Sub Band Image ,Video Coding and Wavelet Based Compression. It also includes the clear image.
The document discusses various types of cybercrime such as hacking, denial of service attacks, identity theft, and computer vandalism. It describes how computers can be used as weapons to commit real-world crimes like credit card fraud and cyber terrorism, and how they can be targets of attacks like hacking, viruses, and denial of service attacks. Examples of specific cybercrimes discussed include ransomware attacks, cyberbullying through social media, and cyber terrorism threatening individuals online. The document concludes by noting that cybercrime is growing and evolving rapidly.
Dynamic programming is a mathematical optimization method and computer programming technique used to solve complex problems by breaking them down into simpler subproblems. It was developed by Richard Bellman in the 1950s and has been applied in many fields. Dynamic programming problems can be solved optimally by breaking them into subproblems with optimal substructures that can be solved recursively. It uses techniques like top-down or bottom-up approaches and storing results of subproblems to solve larger problems efficiently by avoiding recomputing the common subproblems. Multistage graphs are a type of problem well-suited for dynamic programming solutions using techniques like greedy algorithms, Dijkstra's algorithm, or dynamic programming to find shortest paths. Traversal and search algorithms like breadth-
This document discusses various image compression standards and techniques. It begins with an introduction to image compression, noting that it reduces file sizes for storage or transmission while attempting to maintain image quality. It then outlines several international compression standards for binary images, photos, and video, including JPEG, MPEG, and H.261. The document focuses on JPEG, describing how it uses discrete cosine transform and quantization for lossy compression. It also discusses hierarchical and progressive modes for JPEG. In closing, the document presents challenges and results for motion segmentation and iris image segmentation.
Language and Processors for Requirements Specificationkirupasuchi1996
This document discusses several languages and processors that have been developed for requirements specification in software development. It describes Problem Statement Language (PSL) and its processor, the Problem Statement Analyzer (PSA), which were developed to allow concise statement and automated analysis of requirements. It also discusses the Requirements Statement Language (RSL) and Requirements Engineering Validation System (REVS). Finally, it provides a brief overview of Structured Analysis and Design Technique (SADT), including its data and activity diagram components.
This document discusses major factors that influence software cost estimation. It identifies programmer ability, product complexity, product size, available time, required reliability, and level of technology as key factors. It provides details on how each factor affects software cost, including equations to estimate programming time and development duration based on product type and size. Program complexity is broken into three levels: application, utility, and system software. The document also discusses how underestimating code size and inability to compress schedules can impact cost estimates.
This document discusses Java's GUI components and how to create basic buttons and labels. It provides details on:
- Java's GUI components include labels, text fields, buttons, and containers like frames and panels
- Buttons can be created and added to frames or panels, and listeners can be assigned to detect button clicks
- Labels are used to display text, text fields allow for single-line text input, and text areas are for multi-line text
This document discusses several software design techniques: stepwise refinement, levels of abstraction, structured design, integrated top-down development, and Jackson structured programming. Stepwise refinement is a top-down technique that decomposes a system into more elementary levels. Levels of abstraction designs systems as layers with each level performing services for the next higher level. Structured design converts data flow diagrams into structure charts using design heuristics. Integrated top-down development integrates design, implementation, and testing with a hierarchical structure. Jackson structured programming maps a problem's input/output structures and operations into a program structure to solve the problem.
This document summarizes different types of computer instructions including data transfer instructions, data manipulation instructions, and program control instructions. It provides details on specific instruction types like arithmetic instructions, logical instructions, and shift instructions. It also discusses concepts like microinstructions, address sequencing, conditional branching, subroutines, and the mapping between computer instructions and microinstruction addresses in control memory. Computer and control unit configurations are described including memory units, processor and control registers, and the micro instruction code format.
This document discusses file sharing and secondary storage management in operating systems. It covers several topics:
File sharing allows multiple users to access files, but access rights and simultaneous access must be managed. Access rights include permissions levels from none to deletion. Simultaneous access requires enforcing mutual exclusion to prevent conflicts.
Secondary storage management involves allocating blocks to files from available disk space. File allocation methods include contiguous, chained, and indexed allocation. Contiguous allocates all blocks at once while chained uses pointers between non-contiguous blocks. Indexed addresses problems with the other methods.
Free space is managed using techniques like bit tables to track used/free blocks, chained free portions, or a free block list maintained on disk
A B-tree is a tree data structure that keeps data sorted and allows searches, sequential access, insertions, and deletions in logarithmic time. The key properties of a B-tree are that non-leaf nodes can have between m/2 to m children, leaf nodes have at most m-1 keys, and all leaves are at the same depth. An example 5-order B-tree is shown containing 26 keys partitioned among nodes. The process of inserting and deleting keys may involve splitting or merging nodes to maintain the B-tree properties.
This document discusses different addressing modes for the 8086 microprocessor. It describes six data addressing modes: immediate, direct, register, register indirect, register relative, and based indexed addressing modes. It provides examples of how the effective address is calculated for each mode. The document also discusses four addressing modes for branch instructions: intrasegment direct, intrasegment indirect, intersegment direct, and intersegment indirect. It explains how the instruction pointer (IP) and code segment (CS) register values are modified for branching within and between segments.
This document discusses C++ stream classes and file input/output. It covers the key stream classes like iostream, istream, ostream and their functions. It also discusses file classes like ifstream, ofstream and fstream that are used for file input/output. It provides examples of reading from and writing to files using these classes and their functions like open(), get(), put() etc. It mentions how file pointers are used to manipulate file positions for input/output operations.
Optimizing Feldera: Integrating Advanced UDFs and Enhanced SQL Functionality ...mparmparousiskostas
This report explores our contributions to the Feldera Continuous Analytics Platform, aimed at enhancing its real-time data processing capabilities. Our primary advancements include the integration of advanced User-Defined Functions (UDFs) and the enhancement of SQL functionality. Specifically, we introduced Rust-based UDFs for high-performance data transformations and extended SQL to support inline table queries and aggregate functions within INSERT INTO statements. These developments significantly improve Feldera’s ability to handle complex data manipulations and transformations, making it a more versatile and powerful tool for real-time analytics. Through these enhancements, Feldera is now better equipped to support sophisticated continuous data processing needs, enabling users to execute complex analytics with greater efficiency and flexibility.
Essential Skills for Family Assessment - Marital and Family Therapy and Couns...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Discover the cutting-edge telemetry solution implemented for Alan Wake 2 by Remedy Entertainment in collaboration with AWS. This comprehensive presentation dives into our objectives, detailing how we utilized advanced analytics to drive gameplay improvements and player engagement.
Key highlights include:
Primary Goals: Implementing gameplay and technical telemetry to capture detailed player behavior and game performance data, fostering data-driven decision-making.
Tech Stack: Leveraging AWS services such as EKS for hosting, WAF for security, Karpenter for instance optimization, S3 for data storage, and OpenTelemetry Collector for data collection. EventBridge and Lambda were used for data compression, while Glue ETL and Athena facilitated data transformation and preparation.
Data Utilization: Transforming raw data into actionable insights with technologies like Glue ETL (PySpark scripts), Glue Crawler, and Athena, culminating in detailed visualizations with Tableau.
Achievements: Successfully managing 700 million to 1 billion events per month at a cost-effective rate, with significant savings compared to commercial solutions. This approach has enabled simplified scaling and substantial improvements in game design, reducing player churn through targeted adjustments.
Community Engagement: Enhanced ability to engage with player communities by leveraging precise data insights, despite having a small community management team.
This presentation is an invaluable resource for professionals in game development, data analytics, and cloud computing, offering insights into how telemetry and analytics can revolutionize player experience and game performance optimization.
_Lufthansa Airlines MIA Terminal (1).pdfrc76967005
Lufthansa Airlines MIA Terminal is the highest level of luxury and convenience at Miami International Airport (MIA). Through the use of contemporary facilities, roomy seating, and quick check-in desks, travelers may have a stress-free journey. Smooth navigation is ensured by the terminal's well-organized layout and obvious signage, and travelers may unwind in the premium lounges while they wait for their flight. Regardless of your purpose for travel, Lufthansa's MIA terminal
202406 - Cape Town Snowflake User Group - LLM & RAG.pdfDouglas Day
Content from the July 2024 Cape Town Snowflake User Group focusing on Large Language Model (LLM) functions in Snowflake Cortex. Topics include:
Prompt Engineering.
Vector Data Types and Vector Functions.
Implementing a Retrieval
Augmented Generation (RAG) Solution within Snowflake
Dive into the details of how to leverage these advanced features without leaving the Snowflake environment.
Interview Methods - Marital and Family Therapy and Counselling - Psychology S...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Difference in Differences - Does Strict Speed Limit Restrictions Reduce Road ...ThinkInnovation
Objective
To identify the impact of speed limit restrictions in different constituencies over the years with the help of DID technique to conclude whether having strict speed limit restrictions can help to reduce the increasing number of road accidents on weekends.
Context*
Generally, on weekends people tend to spend time with their family and friends and go for outings, parties, shopping, etc. which results in an increased number of vehicles and crowds on the roads.
Over the years a rapid increase in road casualties was observed on weekends by the Government.
In the year 2005, the Government wanted to identify the impact of road safety laws, especially the speed limit restrictions in different states with the help of government records for the past 10 years (1995-2004), the objective was to introduce/revive road safety laws accordingly for all the states to reduce the increasing number of road casualties on weekends
* The Speed limit restriction can be observed before 2000 year as well, but the strict speed limit restriction rule was implemented from 2000 year to understand the impact
Strategies
Observe the Difference in Differences between ‘year’ >= 2000 & ‘year’ <2000
Observe the outcome from multiple linear regression by considering all the independent variables & the interaction term
Do People Really Know Their Fertility Intentions? Correspondence between Sel...Xiao Xu
Fertility intention data from surveys often serve as a crucial component in modeling fertility behaviors. Yet, the persistent gap between stated intentions and actual fertility decisions, coupled with the prevalence of uncertain responses, has cast doubt on the overall utility of intentions and sparked controversies about their nature. In this study, we use survey data from a representative sample of Dutch women. With the help of open-ended questions (OEQs) on fertility and Natural Language Processing (NLP) methods, we are able to conduct an in-depth analysis of fertility narratives. Specifically, we annotate the (expert) perceived fertility intentions of respondents and compare them to their self-reported intentions from the survey. Through this analysis, we aim to reveal the disparities between self-reported intentions and the narratives. Furthermore, by applying neural topic modeling methods, we could uncover which topics and characteristics are more prevalent among respondents who exhibit a significant discrepancy between their stated intentions and their probable future behavior, as reflected in their narratives.
2. Mining multidimensional association rules involves more than
one dimension or predicate.
EXAMPLE:
Rules relating what a customer buys as well as the customer's
age.
These methods can be organized according to their treatment of
quantitative attributes.
3. MULTIDIMENSIONAL ASSOCIATION
RULES
It implies a single predicate, for the predicate buys,
For instance,
Mining our ABC company database, we may discover the
Boolean association rule "IBM desktop computer“
Which implies "Sony b/w printer" .
It can also be written as
buys(X,"IBM desktop computer:")implies buys(X, "sony b/w
printer")
4. where X is a variable representing customers who purchased
items in AB Company transcations.
It contains a single distinct predicate(e.g..buys) with multiple
occurrences.(i.e..predicate occurs more than once)
Such rules are commonly mined from transactions data.
Rather than using a transactional database, sales and related
information are stared in a relational database or data
warehouse.
Such data stares are multidimensional, by definition.
5. To mine association rules containing multiple predicates,
age(x,"20......29")^occupation(X,“ student") implies buys(X,“
laptop":)
Association rules that involve two or more dimensions or
predicates can be referred to as multidimensional association
rules.
The above rule contains three predicates(age, occupation,and
buys),each of which occurs only once in the rule .
It has no repeated predicates.
Multidimensional association rules with no repeated predicates
are called inter-dimensional association rules.
6. MINING MULTIDIMENSIONAL ASSOCIATION
RULES USING STATIC DISCRETIZATION OF
QUANTITATIVE ATTRIBUTES
Quantitative attributes are discretized prior to mining using
predefined concept hierarchies.
Numeric values are replaced by ranges.
Categorical attributes may also be generalized to higher
conceptual levels if desired.
The resulting task-relavant data are stored in a relational table,
then the a priori algorithm requires a slight modification.
To find all frequent predicate sets rather than frequent
itemsets(i.e.,by searching through all of the relevant
attributes, instead of searching only one attribute, like buys').
7. MINING QUANTITATIVE ASSOCIATION
RULES
Quantitative association rules are multidimensional association
rules in which the numeric attributes are dynamically
discretized during the mining process.
To satisfy some mining creteria, maximizing the confidence or
compactness 'of the rules mined.
In this, we will focus specifically to mine quantitative
association rules having two quantitative attributes.
On the left-hand side of the rule, and one categorical attribute
on the right -hand side of the rule,
8. for example,
Aquan1^Aquan2 implies Acat
Where Aquan1 and Aquan2 are tests on quantitative attribute
ranges(where the ranges are dynamically Determined)
Acat tests a categorical attribute form the task relevant data.
Such rules have been referred to as two-dimensional
quantitative association rules.
They contain two quantitative dimensions.
For instance,
suppose you are curious about the association relationship
between pairs of quantitative attributes, like customer age and
income, and the type of television that customers like to buy.
9. BINNING:
Quantitative attributes can have a very wide range of values
denning their domain.
These intervals are dynamic in that they may be combined
during the mining process.
The partitioning process is referred to as binning ,where the
intervals are considered "bins.“
Three common binning strategies are
Equi width binning:
Where the interval size of each bin is the same.
10. Equi depth binning:
where each bin has approximately the same number of
tuples assigned to it.
Homogeneity-based binning:
where bin size is determined so that the tuples in each bin
are uniformly distributed.
Finding frequent predicate sets:
Once the 2-D array containing the count distribution for
each category is set up this can be scanned in order to find the
frequent predicate sets(those satisfying minimum support)that
also satisfy minimum.
11. MINING DISTANCE-BASED
ASSOCIATION RULES
Quantitative association rules the quantitative attributes are
discretized initially by methods.
The resulting intervals an then combined.
Such an approach, may not capture the semantics of intervals
data-since they do not consider the relative distance between
data points or between intervals.
12. A DISADVANTAGE OF ASSOCIATION
RULES
They do not allow for approximations of attribute values.
Consider the folowing association rule:
Item_type(x,"electronic")^manufacturer(X,"foreign")implies
price(X,200)
Where X is a variable describing items at ABCompany .
In reality, it is more likely that the prices of foreign electronic items are
close to or approximately $200,rather than exactly $20.
It would be useful to have association rules that can express such a notion
of closeness.
The support and confidence measures do not consider.
13. The closeness of values for a given attribute.
This motivates the mining of distance-based association rules,
which captures the semantics of interval data while allowing for
approximation in data values.
A two -phase algorithm can be used to mine distance-based
association rules.
The first phase employs clustering to find the intervals or
clusters.
Adapting to the amount of available memory.
The second phase obtains distance-based association rules by
searching for groups of clusters that occur frequently together.
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