Data mining

Data Mining:
Concepts and Techniques
By Akannsha A. Totewar
Professor at YCCE,
Wanadongari,
Nagpur.

1 Data Mining: Concepts and Techniques November 24, 2012

Chapter 1. Introduction

 Motivation: Why data mining?

 What is data mining?

 Data Mining: On what kind of data?

 Data mining functionality

 Are all the patterns interesting?

 Classification of data mining systems

 Major issues in data mining
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Why Mine Data? Commercial Viewpoint

 Lots of data is being collected
and warehoused
 Web data, e-commerce
 purchases at department/
grocery stores
 Bank/Credit Card
transactions

 Computers have become cheaper and more powerful
 Competitive Pressure is Strong
 Provide better, customized services for an edge (e.g. in
Customer Relationship Management)

Motivation: ―Necessity is the Mother of
Invention‖

 Data explosion problem

 Automated data collection tools and mature database technology

lead to tremendous amounts of data stored in databases, data
warehouses and other information repositories

 We are drowning in data, but starving for knowledge!

 Solution: data mining

 Extraction of interesting knowledge (rules, regularities, patterns,

constraints) from data in large databases

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Evolution of Database Technology
 1960s:
 Data collection, database creation, IMS and network DBMS

 1970s:
 Relational data model, relational DBMS implementation

 1980s:
 RDBMS, advanced data models (extended-relational, OO,
deductive, etc.) and application-oriented DBMS (spatial, scientific,
engineering, etc.)
 1990s—2000s:
 Data mining and data warehousing, multimedia databases, and
Web databases

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What is Data Mining?
 Many Definitions
 Extraction of implicit, previously unknown and
potentially useful information from data

 Exploration & analysis, by automatic or
semi-automatic means, of large quantities of data
in order to discover meaningful patterns

What Is Data Mining?
 Data mining (knowledge discovery in databases):
 Extraction of interesting (non-trivial, implicit, previously
unknown and potentially useful) information or patterns from
data in large databases
 Alternative names :
 Knowledge discovery(mining) in databases (KDD),
knowledge extraction, data/pattern analysis, data
archeology, data dredging, information harvesting, business
intelligence, etc.
 What is not data mining?
 (Deductive) query processing.
 Expert systems or statistical programs

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What is (not) Data Mining?

 What is not Data Mining?  What is Data Mining?

– Look up phone number in
phone directory – Certain names are more prevalent in
certain US locations (O’Brien, O’Rurke,
O’Reilly… in Boston area)
– Query a Web search
engine for information – Group together similar documents returned
about ―Amazon‖ by search engine according to their context
(e.g. Amazon rainforest, Amazon.com,)

Origins of Data Mining
 Draws ideas from machine learning/AI, pattern
recognition, statistics, and database systems
 Traditional Techniques
may be unsuitable due to
 Enormity of data Statistics/ Machine Learning/
AI Pattern
 High dimensionality
Recognition
of data
 Heterogeneous, Data Mining
distributed nature
of data
Database
systems

Decisions in Data Mining
 Databases to be mined
 Relational, transactional, object-oriented, object-relational, active,
spatial, time-series, text, multi-media, heterogeneous, legacy,
WWW, etc.
 Knowledge to be mined
 Characterization, discrimination, association, classification,
clustering, trend, deviation and outlier analysis, etc.
 Multiple/integrated functions and mining at multiple levels
 Techniques utilized
 Database-oriented, data warehouse (OLAP), machine learning,
statistics, visualization, neural network, etc.
 Applications adapted
 Retail, telecommunication, banking, fraud analysis, DNA mining, stock
market analysis, Web mining, Weblog analysis, etc.

Data Mining Tasks
 Prediction Tasks
 Use some variables to predict unknown or future values of
other variables
 Description Tasks
 Find human-interpretable patterns that describe the data.

Common data mining tasks
 Classification [Predictive]
 Clustering [Descriptive]
 Association Rule Discovery [Descriptive]
 Sequential Pattern Discovery [Descriptive]
 Regression [Predictive]
 Deviation Detection [Predictive]

Data Mining— Potential
Applications
 Database analysis and decision support
◦ Market analysis and management

 target marketing, customer relation management, market
basket analysis, cross selling, market segmentation
◦ Risk analysis and management

 Forecasting, customer retention, improved underwriting,
quality control, competitive analysis
◦ Fraud detection and management

 Other Applications
◦ Text mining (news group, email, documents) and Web analysis.
◦ Intelligent query answering

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2

Other Applications
 Sports
 IBM Advanced Scout analyzed NBA game statistics (shots
blocked, assists, and fouls) to gain competitive advantage for
New York Knicks and Miami Heat
 Astronomy
 JPL and the Palomar Observatory discovered 22 quasars with
the help of data mining
 Internet Web Surf-Aid
 IBM Surf-Aid applies data mining algorithms to Web access logs
for market-related pages to discover customer preference and
behavior pages, analyzing effectiveness of Web
marketing, improving Web site organization, etc.

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3

Data Mining: A KDD Process

Pattern Evaluation
◦ Data mining: the core of
knowledge discovery Data Mining
process.
Task-relevant Data

Data Warehouse Selection

Data Cleaning

Data Integration

1
4 Databases

Steps of a KDD Process
 Learning the application domain:
◦ relevant prior knowledge and goals of application
 Creating a target data set: data selection
 Data cleaning and preprocessing: (may take 60% of effort!)
 Data reduction and transformation:
◦ Find useful features, dimensionality/variable reduction, invariant
representation.
 Choosing functions of data mining
◦ summarization, classification, regression, association, clustering.
 Choosing the mining algorithm(s)
 Data mining: search for patterns of interest
 Pattern evaluation and knowledge presentation
◦ visualization, transformation, removing redundant patterns, etc.
 Use of discovered knowledge

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5

Data Mining and Business Intelligence
Increasing potential
to support
business decisions End User
Making
Decisions

Data Presentation Business
Analyst
Visualization Techniques
Data Mining Data
Information Discovery Analyst

Data Exploration
Statistical Analysis, Querying and Reporting

Data Warehouses / Data Marts
OLAP, MDA DBA
Data Sources
1 Paper, Files, Information Providers, Database Systems, OLTP
6

Architecture of a Typical Data Mining
System

Graphical user interface

Pattern evaluation

Data mining engine

Knowledge-base
Database or data
warehouse server
Data cleaning & data integration Filtering

Data
Databases Warehouse
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Data Mining: On What Kind of Data?

 Relational databases
 Data warehouses
 Transactional databases
 Advanced DB and information repositories
 Object-oriented and object-relational databases
 Spatial databases
 Time-series data and temporal data
 Text databases and multimedia databases
 Heterogeneous and legacy databases
 WWW
1
8

Data Mining Functionalities (1)

 Concept description: Characterization and
discrimination
◦ Generalize, summarize, and contrast data
characteristics, e.g., dry vs. wet regions
 Association (correlation and causality)
◦ Multi-dimensional vs. single-dimensional association
◦ age(X, ―20..29‖) ^ income(X, ―20..29K‖)  buys(X,
―PC‖) [support = 2%, confidence = 60%]
◦ contains(T, ―computer‖)  contains(x, ―software‖) [1%,
75%]
1
9


 Classification and Prediction
◦ Finding models (functions) that describe and distinguish classes
or concepts for future prediction
◦ E.g., classify countries based on climate, or classify cars based
on gas mileage
◦ Presentation: decision-tree, classification rule, neural network
◦ Prediction: Predict some unknown or missing numerical values

 Cluster analysis
◦ Class label is unknown: Group data to form new classes, e.g.,
cluster houses to find distribution patterns
◦ Clustering based on the principle: maximizing the intra-class
similarity and minimizing the interclass similarity
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0


 Outlier analysis
 Outlier: a data object that does not comply with the general behavior of
the data
 It can be considered as noise or exception but is quite useful in fraud
detection, rare events analysis

 Trend and evolution analysis
 Trend and deviation: regression analysis

 Sequential pattern mining, periodicity analysis

 Similarity-based analysis

2  Other pattern-directed or statistical analyses
1

Are All the ―Discovered‖ Patterns
Interesting?

 A data mining system/query may generate thousands of patterns,
not all of them are interesting.
◦ Suggested approach: Human-centered, query-based, focused mining

 Interestingness measures: A pattern is interesting if it is easily
understood by humans, valid on new or test data with some degree
of certainty, potentially useful, novel, or validates some hypothesis
that a user seeks to confirm
 Objective vs. subjective interestingness measures:
◦ Objective: based on statistics and structures of patterns, e.g., support,
confidence, etc.
◦ Subjective: based on user’s belief in the data, e.g., unexpectedness,
2 Data Mining: Concepts and Techniques
novelty, actionability, etc. November 24, 2012
2

Can We Find All and Only Interesting
Patterns?

 Find all the interesting patterns: Completeness
◦ Can a data mining system find all the interesting patterns?
◦ Association vs. classification vs. clustering

 Search for only interesting patterns: Optimization
◦ Can a data mining system find only the interesting patterns?
◦ Approaches
 First general all the patterns and then filter out the uninteresting
ones.
 Generate only the interesting patterns—mining query
optimization
3

Data Mining: Confluence of Multiple
Disciplines
Database
Statistics
Technology

Machine
Data Mining Visualization
Learning

Information Other
Science Disciplines
4

Data Mining: Classification
Schemes

 General functionality
 Descriptive data mining

 Predictive data mining

 Different views, different classifications
 Kinds of databases to be mined

 Kinds of knowledge to be discovered

 Kinds of techniques utilized

 Kinds of applications adapted
5

A Multi-Dimensional View of Data
Mining Classification
 Databases to be mined
 Relational, transactional, object-oriented, object-
relational, active, spatial, time-series, text, multi-
media, heterogeneous, legacy, WWW, etc.
 Knowledge to be mined
 Characterization, discrimination, association, classification, cluste
ring, trend, deviation and outlier analysis, etc.
 Multiple/integrated functions and mining at multiple levels
 Techniques utilized
 Database-oriented, data warehouse (OLAP), machine
learning, statistics, visualization, neural network, etc.
 Applications adapted
2  Retail, telecommunication, banking, fraud analysis, DNA mining, stock
Data Mining: Concepts and Techniques November 24, 2012
6 market analysis, Web mining, Weblog analysis, etc.

OLAP Mining: An Integration of Data
Mining and Data Warehousing

 Data mining systems, DBMS, Data warehouse
systems coupling
 No coupling, loose-coupling, semi-tight-coupling, tight-coupling

 On-line analytical mining data
 integration of mining and OLAP technologies

 Interactive mining multi-level knowledge
 Necessity of mining knowledge and patterns at different levels of
abstraction by drilling/rolling, pivoting, slicing/dicing, etc.
 Integration of multiple mining functions
2  Characterized and Techniques
Data Mining: Concepts classification, first clustering and then association
November 24, 2012
7

An OLAM Architecture
Mining query Mining result Layer4
User Interface
User GUI API
Layer3
OLAM OLAP
Engine Engine OLAP/OLAM

Data Cube API

Layer2
MDDB
MDDB
Meta Data

Filtering&Integration Database API Filtering
Layer1
Data cleaning Data
2 Databases Data
Data Mining: Concepts andData integration Warehouse
Techniques November 24, 2012
8 Repository

Major Issues in Data Mining (1)

 Mining methodology and user interaction
 Mining different kinds of knowledge in databases
 Interactive mining of knowledge at multiple levels of abstraction
 Incorporation of background knowledge
 Data mining query languages and ad-hoc data mining
 Expression and visualization of data mining results
 Handling noise and incomplete data
 Pattern evaluation: the interestingness problem

 Performance and scalability
 Efficiency and scalability of data mining algorithms

2
 Parallel, distributed and incremental mining methods
9

Major Issues in Data Mining (2)

 Issues relating to the diversity of data types
 Handling relational and complex types of data
 Mining information from heterogeneous databases and global
information systems (WWW)
 Issues related to applications and social impacts
 Application of discovered knowledge
 Domain-specific data mining tools
 Intelligent query answering
 Process control and decision making
 Integration of the discovered knowledge with existing knowledge: A
knowledge fusion problem
3
 Protection of data security, integrity, and privacy
0

Summary
 Data mining: discovering interesting patterns from large amounts of
data
 A natural evolution of database technology, in great demand, with
wide applications
 A KDD process includes data cleaning, data integration, data
selection, transformation, data mining, pattern evaluation, and
knowledge presentation
 Mining can be performed in a variety of information repositories
 Data mining functionalities:
characterization, discrimination, association, classification, clustering,
outlier and trend analysis, etc.
 Classification of data mining systems
1  Major issues in data mining

A Brief History of Data Mining Society

 1989 IJCAI Workshop on Knowledge Discovery in Databases
(Piatetsky-Shapiro)
 Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991)

 1991-1994 Workshops on Knowledge Discovery in Databases
 Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-
Shapiro, P. Smyth, and R. Uthurusamy, 1996)
 1995-1998 International Conferences on Knowledge Discovery in
Databases and Data Mining (KDD’95-98)
 Journal of Data Mining and Knowledge Discovery (1997)

 1998 ACM SIGKDD, SIGKDD’1999-2001 conferences, and SIGKDD
Explorations
 More conferences on data mining
3
 Data Mining: Concepts and Techniques (IEEE) ICDM, etc.
PAKDD, PKDD, SIAM-Data Mining, November 24, 2012
2

Where to Find References?

 Data mining and KDD (SIGKDD member CDROM):
 Conference proceedings: KDD, and others, such as PKDD, PAKDD, etc.
 Journal: Data Mining and Knowledge Discovery
 Database field (SIGMOD member CD ROM):
 Conference proceedings: ACM-SIGMOD, ACM-PODS, VLDB, ICDE, EDBT,
DASFAA
 Journals: ACM-TODS, J. ACM, IEEE-TKDE, JIIS, etc.
 AI and Machine Learning:
 Conference proceedings: Machine learning, AAAI, IJCAI, etc.
 Journals: Machine Learning, Artificial Intelligence, etc.
 Statistics:
 Conference proceedings: Joint Stat. Meeting, etc.
 Journals: Annals of statistics, etc.
 Visualization:
 Conference proceedings: CHI, etc.
3  Data Mining: Concepts and visualization and computer graphics, etc.
Journals: IEEE Trans. Techniques
November 24, 2012
3

References

 U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in
Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996.
 J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan
Kaufmann, 2000.
 T. Imielinski and H. Mannila. A database perspective on knowledge discovery.
Communications of ACM, 39:58-64, 1996.
 G. Piatetsky-Shapiro, U. Fayyad, and P. Smith. From data mining to knowledge
discovery: An overview. In U.M. Fayyad, et al. (eds.), Advances in Knowledge
Discovery and Data Mining, 1-35. AAAI/MIT Press, 1996.
 G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases.
AAAI/MIT Press, 1991.

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http://www.cs.sfu.ca/~han

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Thank you !!!

Data mining

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