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Data Mining Resources

General Pointers

• Artificial Intelligence - A Modern Approach by Stuart Russel
• Business Intelligence and Data Warehousing Glossary
• CRM Guru - Incentive White Papers and Reports
• DAS - Data and Application Security
      Directorate for Computer and Information Science and Engineering Division of
      Information and Intelligent Systems
• Data Mining: Fundamentals and Applications with Examples
• Data Mining and Web Mining: The main web page for information on Data Mining, Knowledge Discovery, Genomic Mining, and Web Mining.
• Data Mining in E-Commerce: An article on data mining applications in E-Commerce.
• Knowledge Discovery and Data Mining - Internet resources in knowledge discovery and data mining
• Machine Learning Resources: A list of links to papers and other resources on machine learning.
• Modeling, Algorithms, and Informatics Group, Computer and Computational Sciences, Los Alamos National Laboratory - The research conducted in the Modeling, Algorithms and Informatics group spans a variety of areas of Computer Science, from application to theory. Projects range from performance analysis and modeling of extreme-scale parallel systems and applications, to pattern recognition to quantum computing.
• Pattern Recognition: Information and Resources about pattern recognition methods and applications.
• STALEMATE KDD Lab: Studies and provides a test-bed for integrated knowledge management-oriented, Web-enabled and pervasively user-interfaced e-business applications featuring:
  • Search engines and data mining on the Internet and intranet
  • Data warehousing and reporting/OLAP 
  • Knowledge-based systems and simulation
  • Intelligent browser-enabled user interfacing
  • System integration

Data Mining Consultant Firms

• CRISP-DM(CRoss Industry Standard Process for Data Mining) - The CRISP-DM project has developed an industry- and tool-neutral Data Mining process model.
• Hewlett Packard Research on Data Mining
• Inxight - Inxight enables organizations to capture, use and reuse the information most important to them - boosting productivity, accelerating new product time-to-market and leveraging past and future investments in information and technology.
• NCR World Wide Services
• Noel Levitz, for Enrollment Management
• Perot Systems
• Pilot Software - A leading provider of business analytics solutions that enable companies worldwide to measure and meet business performance objectives by transforming corporate data into profitable decisions
• Salford Systems

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Research Groups

• Data Mining Research at IBM TJ Watson
• The Harvest System for Information Discovery on the Web
• IBM Almaden's Data Mining Research Group
• Machine Learning Systems Group at JPL
• The National Scalable Cluster Project
• Stanford University Data Mining Project

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Articles and News Releases

• Data Mining, Distributed Networks, and the Laboratory, Digging for information in the laboratory - More efficient with distributed network topology, by Shirley Oakley in Health Management issue
• Data Mining from computer world
• Privacy group files suit over profiling data sold to government by Jennifer Disabantino in Computer World
• Teradata Takes Data Mining Beyond Beer and Diapers by Holly Michael, NCR Corporation - Fraud detection, customer attrition, propensity to buy are among today's applications of data mining.
• Unscrambling CRM, by Michael Vizard, in Info World
• Will Poor Data Ruin Data Warehousing? in DM Direct

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Homepages

• Will Dwinnell, Consultant on Data Mining
• Tom Fawcett, Software Technology Laboratory, Hewlett-Packard Labs
• Jiawei Han, Professor, Computer Science, Specializing in Database systems, Data Mining and data Warehousing, Simon Fraser University
• Y F Leung, Croucher Foundation Postdoctoral Fellow, Bauer Center for Genomics, Research, Harvard University
• Chang-Tien Lu, Assistant Professor, Department of Computer Science, Northern Virginia Center, Virginia Tech

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University Websites

• Laboratory for Knowledge Discovery in Databases, Department of Computing and Information Sciences, Kansas State University
• Data Mining at Central Connecticut State University
• Knowledge Discovery and Data Mining, University of East Anglia, Norwich, UK
• Data Mining and Machine Learning, Intelligent Systems Labs, School of Computing, National University of Singapore

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Tools and Systems

• The CORAL deductive database system
• DataSplash (A database visualization environment)
• The DEVise project at UWisc
• The Harvest Information Discovery System
• Jess, A Java Expert System Shell
• Visualization of association rules
• An example of a visualization interface to a DBMS and here are some more examples.
• The PROLOG manual, any book on PROLOG, or use this site.

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High Performance Computing Data Sites

• The Matrix Market
• The Performance Database Server
• The UCI Machine Learning Database Repository

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Other Miscellaneous Sites

• Decision Support Systems
• The Knowledge Discovery Mine
• National Research Council Canada
• Pilot Software: Pilot Software is a leading provider of business analytics solutions that enable companies worldwide to measure and meet business performance objectives by transforming corporate data into profitable decisions. More than 650 companies around the world use Pilot's solutions including The Boeing Company; Lending Tree and Pacific Life Insurance.
• Introduction to Data Mining
• Some more LINKS for Data Mining
• Online Papers on Artificial Intelligence and Machine Learning

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Reading Materials

• Algorithms and Strategies for Similarity Retrieval
• Algorithms for Associations
• Andrew Moore's Data Mining Tutorials
• Cost Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm
• Data Mining. Hypergraph Traversals, and Machine Learning
• ILP Systems Descriptions
• Lecture Slides : Generalizing Association Rules to Correlations
• Lecture Slides : Generalizing Association Rules to Correlations (Contd.)
• Mining the WWW : Introduction and Main Themes
• More on Associations
• Slides of Data Mining
• Time series analysis
• Time series analysis (matching templates)
• Visualizing data mining results

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Publications, White Papers, and Conference Proceedings

• Selected Publications
• An Overview of Data Mining at Dun & Bradstreet, DIG White Paper 95/01, September 1995, Data Intelligence Group, Pilot Software
• DMLL - 2002
  First International Workshop on Data Mining Lessons Learned (DMLL-2002) held in conjunction with ICML-2002
• Financial Mathematics, Financial Engineering and Risk Management Workshop
• Genetic Algorithm Publications
• Visualizing Data Mining Models by Kurt Thearling, Barry Becker, Dennis DeCoste, Bill Mawby, Michel Pilote, and Dan Sommerfield
• R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules in Large Databases, in Proceedings of the 20th International Conference on Very Large Databases (VLDB'94), pages 487-499, Santiago de Chile, Chile, Sep 1994.
• H. Blockeel, L. De Raedt, N. Jacobs, and B. Demoen, Scaling up ILP by Learning from Interpretations, Data Mining and Knowledge Discovery, Vol. 3, No. 1, pages 59-93, March 1999.
• S. Brin, R. Motwani, and C. Silverstein, Beyond Market Baskets: Generalizing Association Rules to Correlations, in Proceedings of the ACM SIGMOD International Conference on Management of Data, Tucson, AZ, pages 265-276, 1997.
• [AV3] S. Brin, R. Motwani, J.D. Ullman, and S. Tsur, Dynamic Itemset Counting and Implication Rules for Market Basket Data, in Proceedings of the ACM SIGMOD International Conference on Management of Data, Tucson, AZ, pages 255-264, 1997.
• L. DeHaspe, H. Toivonen, and R.D. King, Finding Frequent Substructures in Chemical Compounds, in Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), pages 30-36, New York, NY, Aug 1998.
• J. Gehrke, V. Ganti, R. Ramakrishnan, and W.-Y. Loh, BOAT: Optimistic Decision Tree Construction, in Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 169-180, Philadelphia, PA, 1999.
• T. Imielinski and H. Mannila, A Database Perspective on Knowledge Discovery, Communications of the ACM, Vol. 39, No. 11, pages 58-64, Nov 1996.
• G.H. John, Behind-the-Scenes Data Mining, ACM SIGKDD Explorations, Vol. 1, No. 1, pages 6-8, June 1999.
• N. Lavrac and S. Dzeroski, Inductive Logic Programming: Theory and Applications, Ellis Horwood, NY, 1994.
• N. Lavrac and P.A. Flach, An Extended Transformation Approach to Inductive Logic Programming, ACM Transactions on Computational Logic, Vol. 2, No. 4, pages 458-494, Oct 2001.
• H. Mannila and H. Toivonen, Multiple Uses of Frequent Sets and Condensed Representations, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD'96), Portland, OR, pages 189-194, 1996.
• J.S. Park, M.-S. Chan, and P.S. Yu, An Effective Hash-Based Algorithm for Mining Association Rules, in Proceedings of the ACM SIGMOD International Conference on Management of Data, San Jose, CA, pages 175-186, 1995.
• S. Sarawagi, S. Thomas, and R. Agrawal, Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications, Data Mining and Knowledge Discovery, Vol. 4, Nos. 2-3, pages 89-125, July 2000.
• D. Tsur, J.D. Ullman, S. Abiteboul, C. Clifton, R. Motwani, S. Nestorov, and A. Rosenthal, Query Flocks: A Generalization of Association-Rule Mining, in Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 1-12, Seattle, WA, 1998.

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