Upstate CSCI 525 Data Mining Chapter 1

11
Data Mining:
Concepts and Techniques
(3rd ed.)
— Chapter 1 —
Jiawei Han, Micheline Kamber, and Jian Pei
University of Illinois at Urbana-Champaign &
Simon Fraser University
©2011 Han, Kamber & Pei. All rights reserved.

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Chapter 1. Introduction
 Why Data Mining?
 What Is Data Mining?
 A Multi-Dimensional View of Data Mining
 What Kind of Data Can Be Mined?
 What Kinds of Patterns Can Be Mined?
 What Technology Are Used?
 What Kind of Applications Are Targeted?
 Major Issues in Data Mining
 A Brief History of Data Mining and Data Mining Society
 Summary

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Why Data Mining?
 The Explosive Growth of Data: from terabytes to petabytes
 Data collection and data availability
 Automated data collection tools, database systems, Web,
computerized society
 Major sources of abundant data
 Business: Web, e-commerce, transactions, stocks, …
 Science: Remote sensing, bioinformatics, scientific simulation, …
 Society and everyone: news, digital cameras, YouTube
 We are drowning in data, but starving for knowledge!
 “Necessity is the mother of invention”—Data mining—Automated
analysis of massive data sets

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Evolution of Sciences
 Before 1600, empirical science
 1600-1950s, theoretical science
 Each discipline has grown a theoretical component. Theoretical models often
motivate experiments and generalize our understanding.
 1950s-1990s, computational science
 Over the last 50 years, most disciplines have grown a third, computational branch
(e.g. empirical, theoretical, and computational ecology, or physics, or linguistics.)
 Computational Science traditionally meant simulation. It grew out of our inability to
find closed-form solutions for complex mathematical models.
 1990-now, data science
 The flood of data from new scientific instruments and simulations
 The ability to economically store and manage petabytes of data online
 The Internet and computing Grid that makes all these archives universally accessible
 Scientific info. management, acquisition, organization, query, and visualization tasks
scale almost linearly with data volumes. Data mining is a major new challenge!
 Jim Gray and Alex Szalay, The World Wide Telescope: An Archetype for Online Science,
Comm. ACM, 45(11): 50-54, Nov. 2002

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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.)
 Application-oriented DBMS (spatial, scientific, engineering, etc.)
 1990s:
 Data mining, data warehousing, multimedia databases, and Web
databases
 2000s
 Stream data management and mining
 Data mining and its applications
 Web technology (XML, data integration) and global information systems

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 Summary

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What Is Data Mining?
 Data mining (knowledge discovery from data)
 Extraction of interesting (non-trivial, implicit, previously
unknown and potentially useful) patterns or knowledge from
huge amount of data
 Data mining: a misnomer?
 Alternative names
 Knowledge discovery (mining) in databases (KDD), knowledge
extraction, data/pattern analysis, data archeology, data
dredging, information harvesting, business intelligence, etc.
 Watch out: Is everything “data mining”?
 Simple search and query processing
 (Deductive) expert systems

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Knowledge Discovery (KDD) Process
 This is a view from typical
database systems and data
warehousing communities
 Data mining plays an essential
role in the knowledge discovery
process
Data Cleaning
Data Integration
Databases
Data Warehouse
Task-relevant Data
Selection
Data Mining
Pattern Evaluation

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Example: A Web Mining Framework
 Web mining usually involves
 Data cleaning
 Data integration from multiple sources
 Warehousing the data
 Data cube construction
 Data selection for data mining
 Data mining
 Presentation of the mining results
 Patterns and knowledge to be used or stored into
knowledge-base

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Data Mining in Business Intelligence
Increasing potential
to support
business decisions End User
Business
Analyst
Data
Analyst
DBA
Decision
Making
Data Presentation
Visualization Techniques
Data Mining
Information Discovery
Data Exploration
Statistical Summary, Querying, and Reporting
Data Preprocessing/Integration, Data Warehouses
Data Sources
Paper, Files, Web documents, Scientific experiments, Database Systems

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Example: Mining vs. Data Exploration
 Business intelligence view
 Warehouse, data cube, reporting but not much mining
 Business objects vs. data mining tools
 Supply chain example: tools
 Data presentation
 Exploration

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KDD Process: A Typical View from ML and
Statistics
Input Data Data
Mining
Data Pre-
Processing
Post-
Processing
 This is a view from typical machine learning and statistics communities
Data integration
Normalization
Feature selection
Dimension reduction
Pattern discovery
Association & correlation
Classification
Clustering
Outlier analysis
… … … …
Pattern evaluation
Pattern selection
Pattern interpretation
Pattern visualization

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Example: Medical Data Mining
 Health care & medical data mining – often
adopted such a view in statistics and machine
learning
 Preprocessing of the data (including feature
extraction and dimension reduction)
 Classification or/and clustering processes
 Post-processing for presentation

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 Summary

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Multi-Dimensional View of Data Mining
 Data to be mined
 Database data (extended-relational, object-oriented, heterogeneous,
legacy), data warehouse, transactional data, stream, spatiotemporal,
time-series, sequence, text and web, multi-media, graphs & social
and information networks
 Knowledge to be mined (or: Data mining functions)
 Characterization, discrimination, association, classification,
clustering, trend/deviation, outlier analysis, etc.
 Descriptive vs. predictive data mining
 Multiple/integrated functions and mining at multiple levels
 Techniques utilized
 Data-intensive, data warehouse (OLAP), machine learning, statistics,
pattern recognition, visualization, high-performance, etc.
 Applications adapted
 Retail, telecommunication, banking, fraud analysis, bio-data mining,
stock market analysis, text mining, Web mining, etc.

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 Summary

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Data Mining: On What Kinds of Data?
 Database-oriented data sets and applications
 Relational database, data warehouse, transactional database
 Advanced data sets and advanced applications
 Data streams and sensor data
 Time-series data, temporal data, sequence data (incl. bio-sequences)
 Structure data, graphs, social networks and multi-linked data
 Object-relational databases
 Heterogeneous databases and legacy databases
 Spatial data and spatiotemporal data
 Multimedia database
 Text databases
 The World-Wide Web

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 Summary

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Data Mining Function: (1) Generalization
 Information integration and data warehouse construction
 Data cleaning, transformation, integration, and
multidimensional data model
 Data cube technology
 Scalable methods for computing (i.e., materializing)
multidimensional aggregates
 OLAP (online analytical processing)
 Multidimensional concept description: Characterization
and discrimination
 Generalize, summarize, and contrast data
characteristics, e.g., dry vs. wet region

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Data Mining Function: (2) Association and
Correlation Analysis
 Frequent patterns (or frequent itemsets)
 What items are frequently purchased together in your
Walmart?
 Association, correlation vs. causality
 A typical association rule
 Diaper  Beer [0.5%, 75%] (support, confidence)
 Are strongly associated items also strongly correlated?
 How to mine such patterns and rules efficiently in large
datasets?
 How to use such patterns for classification, clustering,
and other applications?

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Data Mining Function: (3) Classification
 Classification and label prediction
 Construct models (functions) based on some training examples
 Describe and distinguish classes or concepts for future prediction
 E.g., classify countries based on (climate), or classify cars
based on (gas mileage)
 Predict some unknown class labels
 Typical methods
 Decision trees, naïve Bayesian classification, support vector
machines, neural networks, rule-based classification, pattern-
based classification, logistic regression, …
 Typical applications:
 Credit card fraud detection, direct marketing, classifying stars,
diseases, web-pages, …

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Data Mining Function: (4) Cluster Analysis
 Unsupervised learning (i.e., Class label is unknown)
 Group data to form new categories (i.e., clusters), e.g.,
cluster houses to find distribution patterns
 Principle: Maximizing intra-class similarity & minimizing
interclass similarity
 Many methods and applications

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Data Mining Function: (5) Outlier Analysis
 Outlier analysis
 Outlier: A data object that does not comply with the general
behavior of the data
 Noise or exception? ― One person’s garbage could be another
person’s treasure
 Methods: by product of clustering or regression analysis, …
 Useful in fraud detection, rare events analysis

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Time and Ordering: Sequential Pattern,
Trend and Evolution Analysis
 Sequence, trend and evolution analysis
 Trend, time-series, and deviation analysis: e.g.,
regression and value prediction
 Sequential pattern mining
 e.g., first buy digital camera, then buy large SD
memory cards
 Periodicity analysis
 Motifs and biological sequence analysis
 Approximate and consecutive motifs
 Similarity-based analysis
 Mining data streams
 Ordered, time-varying, potentially infinite, data streams

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Structure and Network Analysis
 Graph mining
 Finding frequent subgraphs (e.g., chemical compounds), trees
(XML), substructures (web fragments)
 Information network analysis
 Social networks: actors (objects, nodes) and relationships (edges)
 e.g., author networks in CS, terrorist networks
 Multiple heterogeneous networks
 A person could be multiple information networks: friends,
family, classmates, …
 Links carry a lot of semantic information: Link mining
 Web mining
 Web is a big information network: from PageRank to Google
 Analysis of Web information networks
 Web community discovery, opinion mining, usage mining, …

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Evaluation of Knowledge
 Are all mined knowledge interesting?
 One can mine tremendous amount of “patterns” and knowledge
 Some may fit only certain dimension space (time, location, …)
 Some may not be representative, may be transient, …
 Evaluation of mined knowledge → directly mine only
interesting knowledge?
 Descriptive vs. predictive
 Coverage
 Typicality vs. novelty
 Accuracy
 Timeliness
 …

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 Summary

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Data Mining: Confluence of Multiple Disciplines
Data Mining
Machine
Learning
Statistics
Applications
Algorithm
Pattern
Recognition
High-Performance
Computing
Visualization
Database
Technology

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Why Confluence of Multiple Disciplines?
 Tremendous amount of data
 Algorithms must be highly scalable to handle such as tera-bytes of
data
 High-dimensionality of data
 Micro-array may have tens of thousands of dimensions
 High complexity of data
 Data streams and sensor data
 Time-series data, temporal data, sequence data
 Structure data, graphs, social networks and multi-linked data
 Heterogeneous databases and legacy databases
 Spatial, spatiotemporal, multimedia, text and Web data
 Software programs, scientific simulations
 New and sophisticated applications

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 Summary

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Applications of Data Mining
 Web page analysis: from web page classification, clustering to
PageRank & HITS algorithms
 Collaborative analysis & recommender systems
 Basket data analysis to targeted marketing
 Biological and medical data analysis: classification, cluster analysis
(microarray data analysis), biological sequence analysis, biological
network analysis
 Data mining and software engineering (e.g., IEEE Computer, Aug.
2009 issue)
 From major dedicated data mining systems/tools (e.g., SAS, MS SQL-
Server Analysis Manager, Oracle Data Mining Tools) to invisible data
mining

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 Summary

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Major Issues in Data Mining (1)
 Mining Methodology
 Mining various and new kinds of knowledge
 Mining knowledge in multi-dimensional space
 Data mining: An interdisciplinary effort
 Boosting the power of discovery in a networked environment
 Handling noise, uncertainty, and incompleteness of data
 Pattern evaluation and pattern- or constraint-guided mining
 User Interaction
 Interactive mining
 Incorporation of background knowledge
 Presentation and visualization of data mining results

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Major Issues in Data Mining (2)
 Efficiency and Scalability
 Efficiency and scalability of data mining algorithms
 Parallel, distributed, stream, and incremental mining methods
 Diversity of data types
 Handling complex types of data
 Mining dynamic, networked, and global data repositories
 Data mining and society
 Social impacts of data mining
 Privacy-preserving data mining
 Invisible data mining

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 Summary

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A Brief History of Data Mining Society
 1989 IJCAI Workshop on Knowledge Discovery in Databases
 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)
 ACM SIGKDD conferences since 1998 and SIGKDD Explorations
 More conferences on data mining
 PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM
(2001), etc.
 ACM Transactions on KDD starting in 2007

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Conferences and Journals on Data Mining
 KDD Conferences
 ACM SIGKDD Int. Conf. on
Knowledge Discovery in
Databases and Data Mining (KDD)
 SIAM Data Mining Conf. (SDM)
 (IEEE) Int. Conf. on Data Mining
(ICDM)
 European Conf. on Machine
Learning and Principles and
practices of Knowledge Discovery
and Data Mining (ECML-PKDD)
 Pacific-Asia Conf. on Knowledge
Discovery and Data Mining
(PAKDD)
 Int. Conf. on Web Search and
Data Mining (WSDM)
 Other related conferences
 DB conferences: ACM SIGMOD,
VLDB, ICDE, EDBT, ICDT, …
 Web and IR conferences: WWW,
SIGIR, WSDM
 ML conferences: ICML, NIPS
 PR conferences: CVPR,
 Journals
 Data Mining and Knowledge
Discovery (DAMI or DMKD)
 IEEE Trans. On Knowledge and
Data Eng. (TKDE)
 KDD Explorations
 ACM Trans. on KDD

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Where to Find References? DBLP, CiteSeer, Google
 Data mining and KDD (SIGKDD: CDROM)
 Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc.
 Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDD
 Database systems (SIGMOD: ACM SIGMOD Anthology—CD ROM)
 Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA
 Journals: IEEE-TKDE, ACM-TODS/TOIS, JIIS, J. ACM, VLDB J., Info. Sys., etc.
 AI & Machine Learning
 Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), CVPR, NIPS, etc.
 Journals: Machine Learning, Artificial Intelligence, Knowledge and Information Systems,
IEEE-PAMI, etc.
 Web and IR
 Conferences: SIGIR, WWW, CIKM, etc.
 Journals: WWW: Internet and Web Information Systems,
 Statistics
 Conferences: Joint Stat. Meeting, etc.
 Journals: Annals of statistics, etc.
 Visualization
 Conference proceedings: CHI, ACM-SIGGraph, etc.
 Journals: IEEE Trans. visualization and computer graphics, etc.

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 Summary

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Summary
 Data mining: Discovering interesting patterns and knowledge from
massive amount 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 data
 Data mining functionalities: characterization, discrimination,
association, classification, clustering, outlier and trend analysis, etc.
 Data mining technologies and applications
 Major issues in data mining

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Recommended Reference Books
 S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertex and Semi-Structured Data. Morgan
Kaufmann, 2002
 R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Interscience, 2000
 T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003
 U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and
Data Mining. AAAI/MIT Press, 1996
 U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge
Discovery, Morgan Kaufmann, 2001
 J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 3rd ed., 2011
 D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001
 T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference,
and Prediction, 2nd ed., Springer-Verlag, 2009
 B. Liu, Web Data Mining, Springer 2006.
 T. M. Mitchell, Machine Learning, McGraw Hill, 1997
 G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991
 P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005
 S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998
 I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java
Implementations, Morgan Kaufmann, 2nd ed. 2005

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