Storage Retrieval

Download Data Mining Methods for Knowledge Discovery by Krzysztof J. Cios PDF

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By Krzysztof J. Cios

Data Mining equipment for wisdom Discovery presents an creation to the knowledge mining tools which are usually utilized in the method of data discovery. This publication first elaborates at the basics of every of the information mining equipment: tough units, Bayesian research, fuzzy units, genetic algorithms, computer studying, neural networks, and preprocessing concepts. The ebook then is going directly to completely speak about those tools within the environment of the general strategy of wisdom discovery. various illustrative examples and experimental findings also are incorporated. each one bankruptcy comes with an in depth bibliography.
Data Mining equipment for wisdom Discovery is meant for senior undergraduate and graduate scholars, in addition to a extensive viewers of execs in laptop and data sciences, clinical informatics, and company info systems.

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Extra resources for Data Mining Methods for Knowledge Discovery

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And Lasocki, R.. 1994. On unknown attribute values in functional dependencies. lnt. and Piatetsky-Shapiro, G. 1993. Systems for knowledge discovery in databases. IEEE Trans. G. and Mitchell, T. M. (eds) 1983. Machine Learning: An Artificial Intelligence Approach. Tioga Mingers, 1. 1989. An empirical comparison of selection measures for decision tree induction. Machine Learning, 3: 319- 342 Mitchell, T. 1982. Generalization as search. ArtifiCial Intelligence, 18: 203-226 1 Data Mining and Knowledg Discovery 25 Miller, AJ.

R. 1986(b). Induction of decision trees. Machine Learning, 1: 81-106 Uthurusamy, R. Fayyad, U. and Spangler, S. 1991. Leaming useful rules from inconclusive data. J. (eds) Knowledge Discovery in Databases, AAAIIMIT L. A. Zadeh, 1979. Fuzzy sets and information granularity, In: Gupta M. M. et al. (eds) Advances in 22 1 Data Mining and Knowledge Discovery Fuzzy Set Theory and Applications North Holland, 3- 18 ADDITIONAL READINGS Aimuallim, H. and Dietterich, T. 1991. Learning with many irrelevant features.

73·77 Quinlan, J. and Rivest, R. 1989. Inferring decision trees using the minimum description length principle, Information and Computation. R. 1989. Unknown attribute values in induction. In: Proc: of the Sixth International Machine Learning Workshop (A M. ), (San Mateo, CA), 164-168, Morgan Kaufmann Ramakrishnan, R. et aI.. 1998. Database Management Systems, Mc-Graw Hill Salzberg, S. 1990. Learning with Nested Generalized Exemplars. Kluwer Academic Publishers Shavlik, J. and Diettrich, T. 1990.

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