Data Mining Concepts and Techniques Chapter 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. 15 March 2018 Data Mining: Concepts and Techniques 1

15 March 2018 Data Mining: Concepts and Techniques Brodsky, 2013 2

Big Data Age Booch, 2013 15 March 2018 Data Mining: Concepts and Techniques 3

15 March 2018 Data Mining: Concepts and Techniques Booch, 2013 4

15 March 2018 Data Mining: Concepts and Techniques Booch, 2013 5

15 March 2018 Data Mining: Concepts and Techniques Booch, 2013 6

Big Data Applications 15 March 2018 Data Mining: Concepts and Techniques Brodsky, 2013 7

Brodsky, 2013 15 March 2018 Data Mining: Concepts and Techniques 8

Syllabus n n n Instructor: Prof. Jianlin Cheng My Teaching My Research Office Hours: EBW 109, Mo. We: 4 – 5 TA (Xiaokai Qian) Objectives Text Book Assignments Projects Grading Course web site: http: //calla. rnet. missouri. edu/cheng_courses/datamining 20 16/ 15 March 2018 Data Mining: Concepts and Techniques 9

Coverage of Topics n The book will be covered in two courses at CS, UIUC n n n Introduction to data warehousing and data mining Data mining: Principles and algorithms Our Coverage (both introductory and advanced materials) n Introduction n Data Preprocessing n Mining Frequent Patterns, Association and Correlations n Classification and Prediction n Cluster Analysis n Network mining (advanced) 15 March 2018 Data Mining: Concepts and Techniques 10

Advanced Topics (projects) n Mining data streams, time-series, and sequence data n Mining graphs, social networks n Mining object, spatial, multimedia, text and Web data n n Spatial and spatiotemporal data mining n Multimedia data mining n Text mining n n Mining complex data objects Web mining Applications and trends of data mining n Mining business & biological data n Visual data mining n Data mining and society: Privacy-preserving data mining 15 March 2018 Data Mining: Concepts and Techniques 11

Chapter 1. Introduction n Motivation: Why data mining? n What is data mining? n Data Mining: On what kind of data? n Data mining functionality n Classification of data mining systems n Top-10 most popular data mining algorithms n Major issues in data mining n Overview of the course 15 March 2018 Data Mining: Concepts and Techniques 12

Why Data Mining? n The Explosive Growth of Data: from terabytes to petabytes n Data collection and data availability n Automated data collection tools, database systems, Web, computerized society n Major sources of abundant data n Business: Web, e-commerce, transactions, stocks, … n Science: Remote sensing, bioinformatics, scientific simulation, … n Society and everyone: news, digital cameras, You. Tube n We are drowning in data, but starving for knowledge! n “Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets 15 March 2018 Data Mining: Concepts and Techniques 13

Evolution of Sciences n Before 1600, empirical science n 1600 -1950 s, theoretical science n n 1950 s-1990 s, computational science n n n Each discipline has grown a theoretical component. Theoretical models often motivate experiments and generalize our understanding. 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 n The flood of data from new scientific instruments and simulations n The ability to economically store and manage petabytes of data online n The Internet and computing Grid that makes all these archives universally accessible n n 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 15 March 2018 Data Mining: Concepts and Techniques 14

Evolution of Database Technology n 1960 s: n n 1970 s: n n Data collection, database creation, IMS and network DBMS Relational data model, relational DBMS implementation 1980 s: n n n RDBMS, advanced data models (extended-relational, OO, deductive, etc. ) Application-oriented DBMS (spatial, scientific, engineering, etc. ) 1990 s: n n Data mining, data warehousing, multimedia databases, and Web databases 2000 s n Stream data management and mining n Data mining and its applications n Web technology (XML, data integration) and global information systems 15 March 2018 Data Mining: Concepts and Techniques 15

What Is Data Mining? n Data mining (knowledge discovery from data) n Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data n n Alternative names n n Data mining: a misnomer? 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”? n Simple search and query processing n (Deductive) expert systems 15 March 2018 Data Mining: Concepts and Techniques 16

Knowledge Discovery (KDD) Process n Data mining—core of knowledge discovery process Pattern Evaluation Data Mining Task-relevant Data Selection Data Warehouse Data Cleaning Data Integration Databases 15 March 2018 Data Mining: Concepts and Techniques 17

Data Mining and Business Intelligence Increasing potential to support business decisions Ex: Cancer research Decision Making Data Presentation Visualization Techniques End User Business Analyst Data Mining Information Discovery Data Analyst Data Exploration Statistical Summary, Querying, and Reporting Data Preprocessing/Integration, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems 15 March 2018 Data Mining: Concepts and Techniques DBA 18

Data Mining: Confluence of Multiple Disciplines Database Technology Machine Learning Pattern Recognition 15 March 2018 Statistics Data Mining Algorithm Data Mining: Concepts and Techniques Visualization Other Disciplines 19

Why Not Traditional Data Analysis? n Tremendous amount of data n n High-dimensionality of data n n Algorithms must be highly scalable to handle such as tera-bytes of data Micro-array may have tens of thousands of dimensions High complexity of data n n Time-series data, temporal data, sequence data n Structure data, graphs, social networks and multi-linked data n Heterogeneous databases and legacy databases n Spatial, spatiotemporal, multimedia, text and Web data n n Data streams and sensor data Software programs, scientific simulations New and sophisticated applications 15 March 2018 Data Mining: Concepts and Techniques 20

3 V of Big Data 15 March 2018 http: //www. datasciencecentral. com/forum/topics/the-3 vs-thatdefine-big-data 21 Data Mining: Concepts and Techniques

Multi-Dimensional View of Data Mining n Data to be mined n n Knowledge to be mined n n n Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, etc. Multiple/integrated functions and mining at multiple levels Techniques utilized n n Relational, data warehouse, transactional, stream, objectoriented/relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, etc. Applications adapted n 15 March 2018 Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc. Data Mining: Concepts and Techniques 22

Data Mining: Classification Schemes n General functionality n n n Descriptive data mining (Democrat <-> Republican) Predictive data mining Different views lead to different classifications n Data view: Kinds of data to be mined n Knowledge view: Kinds of knowledge to be discovered n Method view: Kinds of techniques utilized n Application view: Kinds of applications adapted 15 March 2018 Data Mining: Concepts and Techniques 23

Data Mining: On What Kinds of Data? n Database-oriented data sets and applications n n Relational database, data warehouse, transactional database Advanced data sets and advanced applications n Data streams and sensor data n Time-series data, temporal data, sequence data (incl. bio-sequences) n Structure data, graphs, social networks and multi-linked data n Object-relational databases n Heterogeneous databases and legacy databases n Spatial data and spatiotemporal data n Multimedia database n Text databases n The World-Wide Web 15 March 2018 Data Mining: Concepts and Techniques 24

Data Mining Functionalities n Multidimensional concept description: Characterization and discrimination n Good income VS poor? Frequent patterns, association, correlation vs. causality n n Generalize, summarize, and contrast data characteristics, e. g. , human and monkey? Diaper Beer [0. 5%, 75%], Education -> Income Classification and prediction n Construct models (functions) that describe and distinguish classes or concepts for future prediction n n 15 March 2018 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: Concepts and Techniques 25

Data Mining Functionalities (2) n n n Cluster analysis n Class label is unknown: Group data to form new classes, e. g. , cluster houses to find distribution patterns, terrain images? n Maximizing intra-cluster similarity & minimizing intercluster similarity Outlier analysis n Outlier: Data object that does not comply with the general behavior of the data n Noise or exception? Useful in fraud detection, rare events analysis Trend and evolution analysis n Trend and deviation: e. g. , political polls? Who will win republican nomination in 2016? Who will win presidential election? n Sequential pattern mining: e. g. , video mining -> identify objects n Periodicity analysis: climate change? n Similarity-based analysis: future of the Apple company? 15 March 2018 Data Mining: Concepts and Techniques 26

Top-10 Most Popular DM Algorithms: 18 Identified Candidates (I) n n Classification n #1. C 4. 5: Quinlan, J. R. C 4. 5: Programs for Machine Learning. Morgan Kaufmann. , 1993. (e. g. 2012 presidential election) n #2. CART: L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, 1984. n #3. K Nearest Neighbours (k. NN): Hastie, T. and Tibshirani, R. 1996. Discriminant Adaptive Nearest Neighbor Classification. TPAMI. 18(6) n #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 n #5. SVM: Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. Springer-Verlag. n #6. EM: Mc. Lachlan, G. and Peel, D. (2000). Finite Mixture Models. J. Wiley, New York. Association Analysis n #7. Apriori: Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules. In VLDB '94. n #8. FP-Tree: Han, J. , Pei, J. , and Yin, Y. 2000. Mining frequent patterns without candidate generation. In SIGMOD '00. 15 March 2018 Data Mining: Concepts and Techniques 27

The 18 Identified Candidates (II) n n n Link Mining n #9. Page. Rank: Brin, S. and Page, L. 1998. The anatomy of a largescale hypertextual Web search engine. In WWW-7, 1998. n #10. HITS: Kleinberg, J. M. 1998. Authoritative sources in a hyperlinked environment. SODA, 1998. Clustering n #11. K-Means: Mac. Queen, J. B. , Some methods for classification and analysis of multivariate observations, in Proc. 5 th Berkeley Symp. Mathematical Statistics and Probability, 1967. n #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 n #13. Ada. Boost: Freund, Y. and Schapire, R. E. 1997. A decisiontheoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 1 (Aug. 1997), 119 -139. 15 March 2018 Data Mining: Concepts and Techniques 28

The 18 Identified Candidates (III) n n Sequential Patterns n #14. GSP: Srikant, R. and Agrawal, R. 1996. Mining Sequential Patterns: Generalizations and Performance Improvements. In Proceedings of the 5 th International Conference on Extending Database Technology, 1996. n #15. Prefix. Span: J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal and M-C. Hsu. Prefix. Span: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In ICDE '01. Integrated Mining n #16. CBA: Liu, B. , Hsu, W. and Ma, Y. M. Integrating classification and association rule mining. KDD-98. Rough Sets n #17. Finding reduct: Zdzislaw Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Norwell, MA, 1992 Graph Mining n #18. g. Span: Yan, X. and Han, J. 2002. g. Span: Graph-Based Substructure Pattern Mining. In ICDM '02. 15 March 2018 Data Mining: Concepts and Techniques 29

Top-10 Algorithm Finally Selected at ICDM’ 06 n #1: C 4. 5 (61 votes) n #2: K-Means (60 votes) n #3: SVM (58 votes) n #4: Apriori (52 votes) n #5: EM (48 votes) n #6: Page. Rank (46 votes) n #7: Ada. Boost (45 votes) n #7: k. NN (45 votes) n #7: Naive Bayes (45 votes) n #10: CART (34 votes) 15 March 2018 Data Mining: Concepts and Techniques 30

Major Issues in Data Mining n Mining methodology n Mining different kinds of knowledge from diverse data types, e. g. , bio, stream, Web n Performance: efficiency, effectiveness, and scalability n Pattern evaluation: the interestingness problem n Incorporation of background knowledge n Handling noise and incomplete data n n n Parallel, distributed and incremental mining methods Integration of the discovered knowledge with existing one: knowledge fusion User interaction n n Expression and visualization of data mining results n n Data mining query languages and ad-hoc mining Interactive mining of knowledge at multiple levels of abstraction Applications and social impacts n n 15 March 2018 Domain-specific data mining Protection of data security, integrity, and privacy Data Mining: Concepts and Techniques 31

A Brief History of Data Mining Society n 1989 IJCAI Workshop on Knowledge Discovery in Databases n n 1991 -1994 Workshops on Knowledge Discovery in Databases n n Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991) 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) n Journal of Data Mining and Knowledge Discovery (1997) n ACM SIGKDD conferences since 1998 and SIGKDD Explorations n More conferences on data mining n n PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), etc. ACM Transactions on KDD starting in 2007 15 March 2018 Data Mining: Concepts and Techniques 32

Conferences and Journals on Data Mining n KDD Conferences n ACM SIGKDD Int. Conf. on Knowledge Discovery in Databases and Data Mining (KDD) n SIAM Data Mining Conf. (SDM) n (IEEE) Int. Conf. on Data Mining (ICDM) n Conf. on Principles and practices of Knowledge Discovery and Data Mining (PKDD) n Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD) 15 March 2018 n Other related conferences n n VLDB n (IEEE) ICDE n WWW, SIGIR n n ACM SIGMOD ICML, CVPR, NIPS Journals n n Data Mining and Knowledge Discovery (DAMI or DMKD) IEEE Trans. On Knowledge and Data Eng. (TKDE) n KDD Explorations n ACM Trans. on KDD Data Mining: Concepts and Techniques 33

Where to Find References? DBLP, Cite. Seer, Google n Data mining and KDD (SIGKDD: CDROM) n n n Database systems (SIGMOD: ACM SIGMOD Anthology—CD ROM) n n n Conferences: SIGIR, WWW, CIKM, etc. Journals: WWW: Internet and Web Information Systems, Statistics n n n 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 n n 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 n n Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc. Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDD Conferences: Joint Stat. Meeting, etc. Journals: Annals of statistics, etc. Visualization n n 15 March 2018 Conference proceedings: CHI, ACM-SIGGraph, etc. Journals: IEEE Trans. visualization and computer graphics, etc. Data Mining: Concepts and Techniques 34

Architecture: Typical Data Mining System Graphical User Interface Pattern Evaluation Data Mining Engine Knowl edge. Base Database or Data Warehouse Server data cleaning, integration, and selection Database 15 March 2018 Data World-Wide Other Info Repositories Warehouse Web Data Mining: Concepts and Techniques 35

Recommended Reference Books n S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertex and Semi-Structured Data. Morgan Kaufmann, 2002 n R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2 ed. , Wiley-Interscience, 2000 n T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003 n U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996 n U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, 2001 n J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2 nd ed. , 2006 n D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001 n T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2001 n B. Liu, Web Data Mining, Springer 2006. n T. M. Mitchell, Machine Learning, Mc. Graw Hill, 1997 n G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991 n P. -N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005 n S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998 n I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2 nd ed. 2005 15 March 2018 Data Mining: Concepts and Techniques 36

Summary n n n 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. n Data mining systems and architectures n Major issues in data mining 15 March 2018 Data Mining: Concepts and Techniques 37

Programming Tools n n n 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 15 March 2018 Data Mining: Concepts and Techniques 38

Why Data Mining? —Potential Applications n Data analysis and decision support n Market analysis and management n n Risk analysis and management n n n Target marketing, customer relationship management (CRM), market basket analysis, cross selling, market segmentation Forecasting, customer retention, improved underwriting, quality control, competitive analysis Fraud detection and detection of unusual patterns (outliers) Other Applications n Text mining (news group, email, documents) and Web mining n Stream data mining n Bioinformatics and bio-data analysis 15 March 2018 Data Mining: Concepts and Techniques 39

Ex. 1: Market Analysis and Management n n Where does the data come from? —Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies Target marketing n n n 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 n n n Identify the best products for different groups of customers Predict what factors will attract new customers Provision of summary information n Multidimensional summary reports n Statistical summary information (data central tendency and variation) 15 March 2018 Data Mining: Concepts and Techniques 40

Ex. 2: Corporate Analysis & Risk Management n Finance planning and asset evaluation n cash flow analysis and prediction n contingent claim analysis to evaluate assets n cross-sectional and time series analysis (financial-ratio, trend analysis, etc. ) n Resource planning n n summarize and compare the resources and spending Competition n monitor competitors and market directions n group customers into classes and a class-based pricing procedure n set pricing strategy in a highly competitive market 15 March 2018 Data Mining: Concepts and Techniques 41

Ex. 3: Fraud Detection & Mining Unusual Patterns n Approaches: Clustering & model construction for frauds, outlier analysis n Applications: Health care, retail, credit card service, telecomm. n Auto insurance: ring of collisions n Money laundering: suspicious monetary transactions n Medical insurance n n n Professional patients, ring of doctors, and ring of references Unnecessary or correlated screening tests Telecommunications: phone-call fraud n n Retail industry n n 15 March 2018 Phone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm Analysts estimate that 38% of retail shrink is due to dishonest employees Anti-terrorism Data Mining: Concepts and Techniques 42