Chun-Hung Cheng
- 1.R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of the 20th VLDB Conference, pages 487-499, 1994. Google Scholar
Digital Library
- 2.Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos, and Prabhakar Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In Proceedings of the A CM SiGMOD Con- ~erence on Management of Data, Montreal, Canada, 1998. Google ScholarDigital Library
- 3.A. Aho, J. Hopcroft, and J. Ullman. The Design and Analysis of Computer Algorithms. Addison-Welsley, 1974. Google ScholarDigital Library
- 4.P. S. Bradley, Usama Fayyad, and Cory Reina. Scaling clustering algorithms to large databases. In Proceedings of International Conference on Knowledge Discovery and Data Mining KDD-98, AAAI Press, 1998.Google Scholar
- 5.P. S. Bradley, O. L. Mangasarian, and W. Nick Street. Clustering via concave minimization. In M. C. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems -9-, pages 368- 374, Cambridge, MA, 1997. MIT Press.Google Scholar
- 6.Sergey Brin, Rajeev Motwani, and Craig Silverstein. Beyond market baskets: Generalizing association rules to correlations. In Proceedings of the A CM SIGMOD Conference on Management of Data, 1997. Google ScholarDigital Library
- 7.David K. Y. Chiu and Andrew K. C. Wong. Synthesizing knowledge: A cluster analysis approach using event coveting. In IEEE Transactions on Sytems, Man, and Cybernetics, Vol. SMC-16, No. 2, March/April 1986, pages 251-259, 1986. Google ScholarDigital Library
- 8.Thomas M. Cover and Joy A. Thomas. Elements of Information Theory. Wiley Series in Telecommunications, 1991. Google ScholarDigital Library
- 9.I. Csiszgr and J. KSrner. Information Theory: Coding Theorems for Discrete Memoryless System. Academic Press, 1981. Google ScholarDigital Library
- 10.Jay L. Devore. Probability and Statistics for Engineering and the Sciences. Duxbury Press, 4th edition, 1995.Google Scholar
- 11.Martin Ester, Hans-Peter Kriegel, JSrg Sander, Michael Wimmer, and Xiaowei Xu. Incremental clustering for mining in a data warehousing environment. In Proceedings of the ~4th VLDB Conference, New York, USA, 1998. Google ScholarDigital Library
- 12.Martin Ester, Hans-Peter Kriegel, JSrg Sander, and Xiaowei Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of International Conference on Knowledge Discovery and Data Mining KDD-98, AAAI Press, pages 226-231, 1996.Google Scholar
- 13.Takeshi Fukuda, Yasuhiki Morimoto, Shinichi Morishita, and Takeshi Tokuyama. Data mining using twodimensional optimized association rules: Scheme, algorithms, and visualization. In Proceedings of the A CM SIGMOD Conference on Management of Data, 1996. Google ScholarDigital Library
- 14.Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, and Takeshi Tokuyama. Constructing efficient decision trees by using optimized numeric association rules. In Proceedings of the ~2nd VLDB Conference, Mumbai(Bombay), India, 1996. Google ScholarDigital Library
- 15.Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, and Takeshi Tokuyama. Mining optimized association rules for numeric attributes. In Proceedings of the Fifteenth A CM SIGA CT-SIGMOD-SIGART Symposium on Principles of Database Systems, June 1996. Google ScholarDigital Library
- 16.Clark Glymour, David Madigan, Daryl Pregibon, and Padhraic Smyth. Statistical themes and lessons for data mining. Data Mining and Knowledge Discovery, 1:11- 28, 1997. Google ScholarDigital Library
- 17.Sudipto Guha, Rajeev Rastogi, and Kyuseok Shim. CURE: An efficient clustering algorithm for large databases. In Proceedings of the A CM SiGMOD Conference on Management of Data, Montreal, Canada, June 1996. Google ScholarDigital Library
- 18.John A. Hartigan. Clustering algorithms. Wiley, 1975. Google ScholarDigital Library
- 19.Pierre Michaud. Clustering techniques. In Future Generation Computer Systems 13, pages 135-147, 1997. Google ScholarDigital Library
- 20.Raymond T. Ng and Jiawei Han. Efficient and effective clustering methods for spatial data mining. In Proceedings of the 20th VLDB Conference, Santiago, Chile, 1994. Google ScholarDigital Library
- 21.J.R. Quinlan. Induction of decision trees. In Machine Learning, pages 81-106. Kluwer Academic Publishers, 1986. Google Scholar
- 22.J.R. Quinlan. Cd.5: Programs for Machine Learning. Morgan Kaufmann, 1993. Google ScholarDigital Library
- 23.Erich Schikuta. Grid-clustering: An efficient hierarchical clustering method for very large data sets. in Proceedings of Internation Conference on Pattern Recognition (ICPR), pages 101-105, 1996. Google ScholarDigital Library
- 24.Xiaowei Xu, Martin Ester, Hans-Peter Kriegel, and JSrg Sander. A distribution-based clustering algorithm for mining in large spatial databases. In Proceedings of ldth International Conference on Data Engineering (ICDE'98), 1998. Google ScholarDigital Library
- 25.Tian Zhang, Raghu Ramakristman, and Miron Livny. BIRCH: An efficient data clustering method for very large databases. In Proceedings of the A CM SIG- MOD Conference on Management of Data, Montreal, Canada, pages 103-114, June 1996. Google ScholarDigital Library
Index Terms
- Entropy-based subspace clustering for mining numerical data
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