Chapter1_IntroductionIntroductionIntroduction.ppt

February 21, 2024 Data Mining: Concepts and Techniques 1
Data Mining:
Concepts and Techniques
— Chapter 1 —
— Introduction —
Prof. Jianlin Cheng
Department of Computer Science
University of Missouri, Columbia
Slides are adapted from
©Jiawei Han and Micheline Kamber. All rights reserved.

Brodsky, 2013

Big Data Age
Booch, 2013

Booch, 2013

Big Data Applications
Brodsky, 2013

Brodsky, 2013

Syllabus
 Instructor: Prof. Jianlin Cheng
 My Teaching
 My Research
 Office Hours: EBW 109, MoWe: 4 – 5
 TA (Xiaokai Qian)
 Objectives
 Text Book
 Assignments
 Projects
 Grading
 Course web site:
http://calla.rnet.missouri.edu/cheng_courses/datamining20
16/

Coverage of Topics
 The book will be covered in two courses at CS, UIUC
 Introduction to data warehousing and data mining
 Data mining: Principles and algorithms
 Our Coverage (both introductory and advanced materials)
 Introduction
 Data Preprocessing
 Mining Frequent Patterns, Association and Correlations
 Classification and Prediction
 Cluster Analysis
 Network mining (advanced)

Advanced Topics (projects)
 Mining data streams, time-series, and sequence data
 Mining graphs, social networks
 Mining object, spatial, multimedia, text and Web data
 Mining complex data objects
 Spatial and spatiotemporal data mining
 Multimedia data mining
 Text mining
 Web mining
 Applications and trends of data mining
 Mining business & biological data
 Visual data mining
 Data mining and society: Privacy-preserving data mining

Chapter 1. Introduction
 Motivation: Why data mining?
 What is data mining?
 Data Mining: On what kind of data?
 Data mining functionality
 Classification of data mining systems
 Top-10 most popular data mining algorithms
 Major issues in data mining
 Overview of the course

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

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

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

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

Knowledge Discovery (KDD) Process
 Data mining—core of
knowledge discovery
process
Data Cleaning
Data Integration
Databases
Data Warehouse
Task-relevant Data
Selection
Data Mining
Pattern Evaluation

Data Mining and 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
Ex: Cancer research

Data Mining: Confluence of Multiple Disciplines
Data Mining
Database
Technology Statistics
Machine
Learning
Pattern
Recognition
Algorithm
Other
Disciplines
Visualization

Why Not Traditional Data Analysis?
 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

3V of Big Data
http://www.datasciencecentral.com/forum/topics/the-3vs-that-
define-big-data

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

Data Mining: Classification Schemes
 General functionality
 Descriptive data mining (Democrat <-> Republican)
 Predictive data mining
 Different views lead to different classifications
 Data view: Kinds of data to be mined
 Knowledge view: Kinds of knowledge to be discovered
 Method view: Kinds of techniques utilized
 Application view: Kinds of applications adapted

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

Data Mining Functionalities
 Multidimensional concept description: Characterization and
discrimination
 Generalize, summarize, and contrast data characteristics, e.g.,
human and monkey?
 Good income VS poor?
 Frequent patterns, association, correlation vs. causality
 Diaper  Beer [0.5%, 75%], Education -> Income
 Classification and prediction
 Construct models (functions) that describe and distinguish classes
or concepts for future prediction
 E.g., classify countries based on (economy), cars based on (gas
mileage), internet news (Google News), product (Amazon)
 Predict some stock price, traffic jam

Data Mining Functionalities (2)
 Cluster analysis
 Class label is unknown: Group data to form new classes, e.g.,
cluster houses to find distribution patterns, terrain images?
 Maximizing intra-cluster similarity & minimizing intercluster
similarity
 Outlier analysis
 Outlier: Data object that does not comply with the general behavior
of the data
 Noise or exception? Useful in fraud detection, rare events analysis
 Trend and evolution analysis
 Trend and deviation: e.g., political polls? Who will win republican
nomination in 2016? Who will win presidential election?
 Sequential pattern mining: e.g., video mining -> identify objects
 Periodicity analysis: climate change?
 Similarity-based analysis: future of the Apple company?

Top-10 Most Popular DM Algorithms:
18 Identified Candidates (I)
 Classification
 #1. C4.5: Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan
Kaufmann., 1993. (e.g. 2012 presidential election)
 #2. CART: L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification
and Regression Trees. Wadsworth, 1984.
 #3. K Nearest Neighbours (kNN): Hastie, T. and Tibshirani, R. 1996.
Discriminant Adaptive Nearest Neighbor Classification. TPAMI. 18(6)
 #4. Naive Bayes Hand, D.J., Yu, K., 2001. Idiot's Bayes: Not So Stupid
After All? Internat. Statist. Rev. 69, 385-398.
 Statistical Learning
 #5. SVM: Vapnik, V. N. 1995. The Nature of Statistical Learning Theory.
Springer-Verlag.
 #6. EM: McLachlan, G. and Peel, D. (2000). Finite Mixture Models. J.
Wiley, New York. Association Analysis
 #7. Apriori: Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms
for Mining Association Rules. In VLDB '94.
 #8. FP-Tree: Han, J., Pei, J., and Yin, Y. 2000. Mining frequent patterns
without candidate generation. In SIGMOD '00.

The 18 Identified Candidates (II)
 Link Mining
 #9. PageRank: Brin, S. and Page, L. 1998. The anatomy of a
large-scale hypertextual Web search engine. In WWW-7, 1998.
 #10. HITS: Kleinberg, J. M. 1998. Authoritative sources in a
hyperlinked environment. SODA, 1998.
 Clustering
 #11. K-Means: MacQueen, J. B., Some methods for classification
and analysis of multivariate observations, in Proc. 5th Berkeley
Symp. Mathematical Statistics and Probability, 1967.
 #12. BIRCH: Zhang, T., Ramakrishnan, R., and Livny, M. 1996.
BIRCH: an efficient data clustering method for very large
databases. In SIGMOD '96.
 Bagging and Boosting
 #13. AdaBoost: Freund, Y. and Schapire, R. E. 1997. A decision-
theoretic generalization of on-line learning and an application to
boosting. J. Comput. Syst. Sci. 55, 1 (Aug. 1997), 119-139.

The 18 Identified Candidates (III)
 Sequential Patterns
 #14. GSP: Srikant, R. and Agrawal, R. 1996. Mining Sequential Patterns:
Generalizations and Performance Improvements. In Proceedings of the
5th International Conference on Extending Database Technology, 1996.
 #15. PrefixSpan: J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U.
Dayal and M-C. Hsu. PrefixSpan: Mining Sequential Patterns Efficiently by
Prefix-Projected Pattern Growth. In ICDE '01.
 Integrated Mining
 #16. CBA: Liu, B., Hsu, W. and Ma, Y. M. Integrating classification and
association rule mining. KDD-98.
 Rough Sets
 #17. Finding reduct: Zdzislaw Pawlak, Rough Sets: Theoretical Aspects of
Reasoning about Data, Kluwer Academic Publishers, Norwell, MA, 1992
 Graph Mining
 #18. gSpan: Yan, X. and Han, J. 2002. gSpan: Graph-Based Substructure
Pattern Mining. In ICDM '02.

Top-10 Algorithm Finally Selected at
ICDM’06
 #1: C4.5 (61 votes)
 #2: K-Means (60 votes)
 #3: SVM (58 votes)
 #4: Apriori (52 votes)
 #5: EM (48 votes)
 #6: PageRank (46 votes)
 #7: AdaBoost (45 votes)
 #7: kNN (45 votes)
 #7: Naive Bayes (45 votes)
 #10: CART (34 votes)

Major Issues in Data Mining
 Mining methodology
 Mining different kinds of knowledge from diverse data types, e.g., bio, stream,
Web
 Performance: efficiency, effectiveness, and scalability
 Pattern evaluation: the interestingness problem
 Incorporation of background knowledge
 Handling noise and incomplete data
 Parallel, distributed and incremental mining methods
 Integration of the discovered knowledge with existing one: knowledge fusion
 User interaction
 Data mining query languages and ad-hoc mining
 Expression and visualization of data mining results
 Interactive mining of knowledge at multiple levels of abstraction
 Applications and social impacts
 Domain-specific data mining
 Protection of data security, integrity, and privacy

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

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)
 Conf. on Principles and
practices of Knowledge
Discovery and Data Mining
(PKDD)
 Pacific-Asia Conf. on
Knowledge Discovery and Data
Mining (PAKDD)
 Other related conferences
 ACM SIGMOD
 VLDB
 (IEEE) ICDE
 WWW, SIGIR
 ICML, CVPR, NIPS
 Journals
 Data Mining and Knowledge
Discovery (DAMI or DMKD)
 IEEE Trans. On Knowledge
and Data Eng. (TKDE)
 KDD Explorations
 ACM Trans. on KDD

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.

Architecture: Typical Data Mining System
data cleaning, integration, and selection
Database or Data
Warehouse Server
Data Mining Engine
Pattern Evaluation
Graphical User Interface
Knowl
edge-
Base
Database
Data
Warehouse
World-Wide
Web
Other Info
Repositories

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, 2nd ed., 2006
 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, Springer-Verlag, 2001
 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

Summary
 Data mining: Discovering interesting patterns from large amounts of
data
 A natural evolution of database technology and machine learning, 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.
 Data mining systems and architectures
 Major issues in data mining

Programming Tools
 Any general programming languages: C/C++,
Java, Perl, Python
 Specialized language packages: R, Matlab (or
Octave), Mathematica
 Machine learning and data mining packages:
Weka, NNClass, SVMlight
 Homework submission:
mudatamining@gmail.com
 Due by Feb. 8, 2016

Why Data Mining?—Potential Applications
 Data analysis and decision support
 Market analysis and management
 Target marketing, customer relationship management (CRM),
market basket analysis, cross selling, market segmentation
 Risk analysis and management
 Forecasting, customer retention, improved underwriting,
quality control, competitive analysis
 Fraud detection and detection of unusual patterns (outliers)
 Other Applications
 Text mining (news group, email, documents) and Web mining
 Stream data mining
 Bioinformatics and bio-data analysis

Ex. 1: Market Analysis and Management
 Where does the data come from?—Credit card transactions, loyalty cards,
discount coupons, customer complaint calls, plus (public) lifestyle studies
 Target marketing
 Find clusters of “model” customers who share the same characteristics: interest,
income level, spending habits, etc.
 Determine customer purchasing patterns over time
 Cross-market analysis—Find associations/co-relations between product sales,
& predict based on such association
 Customer profiling—What types of customers buy what products (clustering
or classification)
 Customer requirement analysis
 Identify the best products for different groups of customers
 Predict what factors will attract new customers
 Provision of summary information
 Multidimensional summary reports
 Statistical summary information (data central tendency and variation)

Ex. 2: Corporate Analysis & Risk Management
 Finance planning and asset evaluation
 cash flow analysis and prediction
 contingent claim analysis to evaluate assets
 cross-sectional and time series analysis (financial-ratio, trend
analysis, etc.)
 Resource planning
 summarize and compare the resources and spending
 Competition
 monitor competitors and market directions
 group customers into classes and a class-based pricing procedure
 set pricing strategy in a highly competitive market

Ex. 3: Fraud Detection & Mining Unusual Patterns
 Approaches: Clustering & model construction for frauds, outlier analysis
 Applications: Health care, retail, credit card service, telecomm.
 Auto insurance: ring of collisions
 Money laundering: suspicious monetary transactions
 Medical insurance
 Professional patients, ring of doctors, and ring of references
 Unnecessary or correlated screening tests
 Telecommunications: phone-call fraud
 Phone call model: destination of the call, duration, time of day or
week. Analyze patterns that deviate from an expected norm
 Retail industry
 Analysts estimate that 38% of retail shrink is due to dishonest
employees
 Anti-terrorism

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