EE380L: Data Mining Reading List 1 This is a list of broad, survey-type but useful articles, that are included with the lecture notes. Be aware of the copyright notice when you use these materials.

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1. The New Jersey Data Reduction Report
   Daniel Barbara, William DuMouchel, Christos Faloutsos, Peter J. Haas, Joseph M. Hellerstein, Yannis Ioannisdis, H. V. Jagadish, Theodore Johnson, Raymond Ng, Viswanath Poosala, Kenneth A. Ross, and Kenneth C. Servcik
   IEEE Bulletin of the Technical Committee on Data Engineering, 20(4), Dec, 1997, pp 3-45

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2. Statistical themes and lessons for data mining
   Daryl Pregibon, Clark Glymour, David Madigan and Padhraic Smyth
   In Proc. Second International Conference on Knowldege Discovery and Data Mining, pp 25-42, 1996

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3. An Overview of Predictive Learning and Function Approximation
   J. H. Friedman
   In V. Cherkassky, J.H. Friedman, and H. Wechsler, editors, From Statistics to Neural Networks, Proc. NATO/ASI Workshop, pp 1-61, Springer Verlag, 1994

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4. Chameleon: Hierarchical Clustering Using Dynamic Modeling
   George Karypis, Eui-Hong (Sam) Han, Vipin Kumar
   IEEE Computer, 32(8), 1999 Aug, pp. 68-75

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5. Leo Breiman
   in COMBINING ARTIFICIAL NEURAL NETS: Ensemble and Modular Multi-Net Systems
   Edited: Amanda Sharkey Publisher: Springer-Verlag London Ltd 1999

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6. Mining Very Large Databases
   Venkatesh Ganti, Johannes Gehrke, Raghu Ramakrishnan
   IEEE Computer, 32(8), Aug, 1999, pp. 38-45
   

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7. Relationship-based Clustering and Visualization for High-dimensional Data Mining
   A. Strehl and J. Ghosh
   Tech. Report, ECE/UT