Chapter 1 Introduction n Motivation Why data mining

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 18 March 2018 Data Mining: Concepts and Techniques 1

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, Facebook 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 18 March 2018 Data Mining: Concepts and Techniques 2

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 18 March 2018 Data Mining: Concepts and Techniques 3

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 18 March 2018 Data Mining: Concepts and Techniques 4

Data Mining: Confluence of Multiple Disciplines Database Technology Machine Learning Pattern Recognition 18 March 2018 Statistics Data Mining Algorithm Data Mining: Concepts and Techniques Visualization Other Disciplines 5

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 18 March 2018 Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc. Data Mining: Concepts and Techniques 6

Data Mining: Classification Schemes n General functionality n n n Descriptive data mining 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 18 March 2018 Data Mining: Concepts and Techniques 7

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 18 March 2018 Data Mining: Concepts and Techniques 8

Data Mining Functionalities n Multidimensional concept description: Characterization and discrimination n n Frequent patterns, association, correlation vs. causality n n Generalize, summarize, and contrast data characteristics, e. g. , dry vs. wet regions Diaper Beer [0. 5%, 75%] (Correlation or causality? ) Classification and prediction n Construct models (functions) that describe and distinguish classes or concepts for future prediction n n 18 March 2018 E. g. , classify countries based on (climate), or classify cars based on (gas mileage) Predict some unknown or missing numerical values Data Mining: Concepts and Techniques 9

Data Mining Functionalities (2) n n Cluster analysis n Class label is unknown: Group data to form new classes, e. g. , cluster houses to find distribution patterns n Maximizing intra-class similarity & minimizing interclass 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. , regression analysis n Sequential pattern mining: e. g. , digital camera large SD memory n Periodicity analysis n Similarity-based analysis Other pattern-directed or statistical analyses 18 March 2018 Data Mining: Concepts and Techniques 10

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. 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. 18 March 2018 Data Mining: Concepts and Techniques 11

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. 18 March 2018 Data Mining: Concepts and Techniques 12

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. 18 March 2018 Data Mining: Concepts and Techniques 13

Top-10 Algorithm Finally Selected at ICDM’ 06 n #1: C 4. 5 (61 votes) (Classification) n #2: K-Means (60 votes) (Clustering) n #3: SVM (58 votes) (Classification) n #4: Apriori (52 votes) (Frequent pattern) n #5: EM (48 votes) n #6: Page. Rank (46 votes) (Web mining) n #7: Ada. Boost (45 votes) (Classification) n #7: k. NN (45 votes) (Classification) n #7: Naive Bayes (45 votes) (Classification) n #10: CART (34 votes) (Classification) 18 March 2018 (Clustering) Data Mining: Concepts and Techniques 14

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 18 March 2018 Domain-specific data mining & invisible data mining Protection of data security, integrity, and privacy Data Mining: Concepts and Techniques 15

Summary n n n 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. n Data mining systems and architectures n Major issues in data mining 18 March 2018 Data Mining: Concepts and Techniques 16

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 18 March 2018 Data Mining: Concepts and Techniques 17

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) 18 March 2018 Data Mining: Concepts and Techniques 18

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 18 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 19

KDD Process: Several Key Steps n Learning the application domain n relevant prior knowledge and goals of application n Creating a target data set: data selection n Data cleaning and preprocessing: (may take 60% of effort!) n Data reduction and transformation n n Find useful features, dimensionality/variable reduction, invariant representation Choosing functions of data mining n summarization, classification, regression, association, clustering n Choosing the mining algorithm(s) n Data mining: search for patterns of interest n Pattern evaluation and knowledge presentation n n visualization, transformation, removing redundant patterns, etc. Use of discovered knowledge 18 March 2018 Data Mining: Concepts and Techniques 20

Big data (海量資料） n (Gartner) Big data is high-volume, highvelocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. Volume: terabytes to petabytes size of data Velocity: high speed of data in and out Variety: many types of data Veracity : true data (4 V—IBM) n Example: Facebook data n n 18 March 2018 Data Mining: Concepts and Techniques 21

Big Data 4 V MNIST: 60, 000 Volume digits handwriting Samples Image. Net: 14, 197, 122 image Velocity BIG DATA Current focus Variety Veracity 18 March 2018 Data Mining: Concepts and Techniques 22

AI, ML, DL Artificial Intelligence Deep learning: 1. Conventional neural networks (CNN) 2. Recurrent neural network (RNN) 3. Long Short-Term Memory (LSTM) 4. Deep Belief network (DBNs) 5. Deep Boltzmann Machine (DBMs) 18 March 2018 Machine Learning Neural Networks Deep Learning Data Mining: Concepts and Techniques Machine Learning: 1. Regression 2. Decision tree 3. Support vector machine 4. Artificial neural network 5. Deep learning 6. Clustering 7. Bayesian algorithm 8. Genetic algorithm 9. Nature-inspired algorithms 10. Association rules 11. Regularization (to prevent overfitting) 12. Ensemble algorithms 23

AI redefine n AI = Deep Learning + Big Data n AI is becoming part of our lives! 18 March 2018 Data Mining: Concepts and Techniques 24

DL application n n Machine translation! Self-driving cars 18 March 2018 Data Mining: Concepts and Techniques 25

Robot vision and recognition: Harvest robot for peppers. Wageningen University, the Netherlands Vision for self-driving cars 26

DM Tools n n Languages: R, Matlab, Mathematica II A language you must learn Python for deep learning (tensorflow)!! One handy tool: Weka n We will use R & Weka in this course or maybe Mathematica (for CNN) n For deep learning, you may want to install a GPU n 18 March 2018 Data Mining: Concepts and Techniques 27