1. 1 1 Data Mining: Concepts and Techniques (3 rd ed.) — Chapter 1: Introduction — Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University ©2010 Han, Kamber & Pei. All rights reserved.

2. 2 2 CPSC 5306 Research Topics in Data management Research Topic for 2016: Data Mining Instructor: Kalpdrum Passi Office: FA 380 Home page: www.cs.laurentian.ca/kpassi/cpsc5306.htmlwww.cs. Teaching: Tue/Thu 4:00-5:30 pm (F 443) Office hours: Thu 3:00 – 4.00 pm (FA 380) Prerequisites General background: Knowledge on statistics, machine learning, and data and information systems will help understand the course materials

3. 3 3 Textbook & Recommended Reference Books Textbook: Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 3 nd ed., Morgan Kaufmann, 2011 Recommended reference books C. M. Bishop, Pattern Recognition and Machine Learning, Springer 2007. S. Chakrabarti, "Mining the Web: Statistical Analysis of Hypertext and Semi-Structured Data", Morgan Kaufmann, 2002 R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Inter- science, 2001. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction,2 nd ed., Springer-Verlag, 2009. B. Liu, Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Springer, 2006 C. D. Manning, P. Raghavan, and H. Schutze, Introduction to Information Retrieval, Cambridge Univ. Press, 2008. P.-N.Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, Addison-Wesley, 2006 (good preparation materials). I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2nd ed., 2005

4. 4 4 Reference Papers Major conference proceedings that will be used DM conferences: ACM SIGKDD (KDD), ICDM (IEEE, Int. Conf. Data Mining), SDM (SIAM Data Mining), PKDD (Principles KDD)/ECML, PAKDD (Pacific-Asia) DB conferences: ACM SIGMOD, VLDB, ICDE ML conferences: NIPS, ICML IR conferences: SIGIR, CIKM Web conferences: WWW, WSDM Other related conferences and journals IEEE TKDE, ACM TKDD, DMKD, ML, Use course Web page, DBLP, Google Scholar, Citeseer

5. 5 5 Course Evaluation Theme-based survey paper: 10% Midterm: 30% Final Exam: 30% Final course project: 30% Groups of 2 students due at the end of semester, but a one-page proposal will be due at the end of the 3rd week, evaluation based on (i) technical innovation, (ii) thoroughness of the work, and (iii) clarity of presentation.

6. 6 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

7. 7 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

8. 8 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

9. 9 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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11. 11 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

12. 12 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

13. 13 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

14. 14 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

15. 15 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

16. 16 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

17. 17 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

18. 18 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

19. 19 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

20. 20 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.

21. 21 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

22. 22 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

23. 23 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

24. 24 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

25. 25 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?

26. 26 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, k-nearest neighbor, … Typical applications: Credit card fraud detection, direct marketing, classifying stars, diseases, web-pages, …

27. Data Mining Function: (3) Regression Classification predicts categorical (discrete, unordered) labels Regression models continuous-valued functions Predict missing or unavailable numerical data values Prediction refers to both numeric and class labels Regression analysis is a statistical methodology that is most often used for numeric prediction Classification and regression may be preceded by relevance analysis Identify attributes that are relevant Example: Classify based on three types of responses to sales campaign – good, mild, none Derive a model based on descriptive features of items – price, brand, place_made, type, category Predict the amount of revenue that each item will generate during upcoming sales based on previous sales data – regression analysis 27

28. 28 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 Example: Cluster analysis on customer data to identify homogeneous subpopulations of customers

29. 29 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, … Detected using statistical tests that assume a distribution or probability model for the data, or using distance measures where objects that are remote from any other cluster Deviation-based methods identify outliers by examining differences in the main characteristics of objects in a group Useful in fraud detection, rare events analysis Example: Uncover fraudulent usage of credit cards by detecting purchases of unusually large amounts for a given accoutn number in comparison to regular charges

30. 30 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

31. 31 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, …

32. 32 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 …

33. 33 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

34. 34 Data Mining: Confluence of Multiple Disciplines Data Mining Machine Learning Statistics Applications Algorithm Pattern Recognition High-Performance Computing Visualization Database Technology

35. 35 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

36. 36 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

37. 37 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

38. 38 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

39. 39 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

40. 40 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

41. 41 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

42. 42 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

43. 43 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

44. 44 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.

45. 45 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, 2 nd ed., 2006 (3ed. 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, 2 nd 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, 2 nd ed. 2005

46. 46 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

47. 47 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

48. 48 Research Frontiers in Advanced Data Mining Technologies and Applications Pattern mining, pattern usage, and pattern understanding Information network analysis Stream data mining Mining moving object data, RFID data, and data from sensor networks Spatiotemporal and multimedia data mining Biological data mining Text and Web mining Data mining for software engineering and computer system analysis Data cube-oriented multidimensional online analytical analysis

49. 49 Pattern Mining & Its Applications Mining approximate and/or colossal patterns Open problems for sequential and graph/structural patterns Mining sequential patterns in long-sequences in text data Mining sequential patterns in long-sequences in biological data Extracting redundancy-aware top-k patterns Pattern compression Pattern-based classification Frequent, discriminative pattern analysis For numerical and high-dimensional data Semantic annotation of frequent patterns Patterns become meaningful/interesting in proper contexts

50. 50 Information Network Analysis Everything is linked! Web, social networks, information networks, biological networks, and physical networks (transportation, power, compter network, etc.) Links as endorsement, containing rich semantic information Mining social network by clustering and simplification Mining heterogeneous networks OLAP information networks Evolution of information networks Privacy and security in information networks

51. 51 Link Analysis and Multi-Relational Mining Cross-relational classification Cross-relational clustering with user's guidance LinkClus: Efficient clustering via heterogeneous semantic links Object Distinction: Distinguishing objects with identical names by link analysis Veracity analysis: Truth discovery with multiple conflicting information providers on the Web Handling time-evolving truth and multi-versioned truth RankClus: Integration of Ranking and Clustering in heterogeneous information networks

52. 52 Stream Data Mining Stream OLAP and stream data cube Stream sample counting and frequent pattern analysis Stream sequential and structured pattern analysis Classification of data streams Classification with skewed data (rare events) Classification with partially labeled data Clustering data streams Stream outlier analysis Integrating with new outliler analysis methods

53. 53 Mining Moving Object Data, RFID Data, and traffic data mining Mining moving object data Trajectory classification, clustering, outlier analysis, and pattern analysis RFID data warehousing and data mining Warehousing and Analysis of Massive RFID Data Sets. Cost-conscious Cleaning of Massive RFID Data Sets. FlowCube: Constructuing RFID FlowCubes for Multi- Dimensional Analysis of Commodity Flows Traffic mining Traffic data cube Traffic pattern and outlier analysis

54. 54 Spatiotemporal and Multimedia Data Mining Multimodal data: spatial, spatiotemploral, image, video, text data Spatial, spatiotemploral and multimedia data warehouse and OLAP Spatial, spatiotemploral, and multimedia frequent pattern and correlation analysis Spatial, spatiotemploral and multimedia data classification Spatial, spatiotemploral and multimedia clustering Spatial, spatiotemploral and multimedia outlier analysis

55. 55 Biological Data Mining Bioinformatics: A driving force in data mining Huge, complex and valuable data sources for data mining The most rewarding frontier for data mining Gene or protein chips, molecular structures, bio-medical data, spatial/image data, Biological sequence analysis Promoters and cis-regulatory analysis Data mining for genomics and proteomics Classification, clustering, outlier analysis Biological graph/network mining Mining structures, linkages and clusters Analysis of biomedical literature databases (MEDLINE, BIOSIS, etc.)

56. 56 Text and Web Mining Document clustering Document classification Document pattern analysis Text- or Topic- cube and text OLAP Web analysis Web community discovery Block-based Web search and block-level link analysis Mining web structures

57. 57 Data Mining for Software Engineering, Computer System and Sensor Network Analysis Software bug mining Statistical Model-based Bug Localization Failure Proximity: A Fault Localization-Based Approach Mining for software plagiarism detection Detection of Software Plagiarism by Procedure Dependency Graph Analysis Mining for structural indexing and similarity search Graph Indexing: A Frequent Structure-based Approach Substructure Similarity Search Sensor Networks Multi-stage approach for sensor network program debugging Data mining in cyber-physical systems

58. 58 Data Cube-Oriented Multidimensional Online Analytical Processing Integration of data cube with data mining Regression Cubes Prediction Cubes Sampling cubes Integration cube and ranking query processing The Ranking Cube Approach ArCube: Aggregation ranking in data cubes High-dimensional OLAP Integration with high-dimensional data mining Multidimensional promotion analysis

59. 59 Privacy-Preserving Data Mining Mining and privacy: Conflicting goals? Powerful data mining tools vs. privacy/security Homeland security and individual privacy Solution—privacy-preserving data mining Encryption of data while preserving mining statistics Data perturbation: adding noise and randomization Data transformation: Projection in different angles while preserving mining results Privacy-preserving mining in distributed environments

60. 60 Invisible Data Mining Data mining as an invisible component of information service Web search engine (link analysis, authoritative pages, user profiles), Google News, adaptive web sites, etc. Query optimization via data mining Improvement of query processing based on history + data Software bug detection and correction Based on software execution history and statistics Data mining: hard to learn and master? Embedded functions: Best way to deploy data mining solutions Making service smart and efficient

61. 61 Integrated Data/Information Systems Today’s information systems: DBMS Future information systems Integration of (semi-structured) database (storage, access), data warehouse (integration, consolidation), and data mining (knowledge discovery) Infrastructure for integrated data/information systems Dynamically determine which functions to evoke Mining query optimization System tuning and adaptation by mining

62. 62 Applications of Data Mining Profit mining, micro-economical model Data mining and network intrusion detection Botnet detection Data mining and collaborative filtering Mining network value of customers

63. 63

64. 64 Mining Biological Data: Coverage Mining DNA, RNA, and proteins: (1) Mining motif patterns, (2) searching homology in large databases, (3) phylogenetic and functional prediction—Exploring sequential and structural data (e.g., protein structure) mining methods Mining gene expression data: (1) clustering gene expression, e.g., for gene regulatory networks, (2) classifying gene expression, e.g., for disease-sensitive gene discovery. Mining mass spectrometry data: Mining and integrating knowledge from biomedical literature Mining inter-domain associations

65. 65 Text Mining Text representation Set-of-words, bag-of-words, vector-space model; the issue of large raw dimensionality Shallow linguistics Phrase detection, part-of-speech tagging, named entity extraction, word sense disambiguation Feature selection and dimensionality reduction PCA, SVD, latent semantic indexing Text clustering agglomerative, k-means, EM; effect of a large number of noise dimensions, partial supervision Document classification Naive Bayes classification: Poor density estimates, small-degree Bayesian belief network induction Discriminative learning: maximum entropy, logistic regression, and support vector learning

66. 66 Mining the Web Web modeling Web as an evolving, collaborative, social network: graph-structure of the Web Data sources: Semi-structured vs. structured, shallow vs. deep Classification and clustering Mining by exploiting text and links for better clustering and classification; unified probabilistic models for text and links Structured data extraction Information extraction, exploiting markup structure to extract structured data from pages meant for human consumption Multidimensional Web databases Automatic construction of multilayered Web information base; discovering entities and relations on the Web Semantic Web Web usage mining and adaptive Web sites

67. 67 Data Mining & System/Software Engineering System engineering Expensive: Construction, maintenance and evolution of sophisticated systems (esp. software systems) Autonomic computing: automate the above process Software systems generates huge volumes of data Abundant and valuable sources for data mining Find rules, regularity, alarms for outliers and bugs System diagnosis, maintenance, and evolution by data mining

68. 68 Grand Challenges in Data Mining Mining sequences, trees, graphs, and complex networks Data mining in data streams and sensor databases Data mining across multiple, heterogeneous data sources Mining in Web and global information systems Bio-medical data mining and bioinformatics Data mining for software/system engineering Mining for data integration and improvement of DBMS functionality Invisible data mining Privacy-preserving data mining Data mining languages, foundations, and breakthroughs