By Oded Maimon, Lior Rokach

ISBN-10: 0387098224

ISBN-13: 9780387098227

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This e-book organizes key techniques, theories, criteria, methodologies, traits, demanding situations and functions of knowledge mining and information discovery in databases. It first surveys, then offers entire but concise algorithmic descriptions of equipment, together with vintage tools plus the extensions and novel equipment built lately. It additionally provides in-depth descriptions of information mining purposes in quite a few interdisciplinary industries.
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Extra resources for Data Mining and Knowledge Discovery Handbook (Springer series in solid-state sciences)

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290-294. Levitin, A. & Redman, T. A Model of the Data (Life) Cycles with Application to Quality, Information and Software Technology 1995; 35(4):217-223. , Sung, S. , & Ling, T. W. A New Efficient Data cleansing Method. Proceedings of Database and Expert Systems Applications (DEXA 2002); 2002 September 2-6; Aix-en-Provence, France. 484-493. Maimon, O. and Rokach, L. Improving supervised learning by feature decomposition, Proceedings of the Second International Symposium on Foundations of Information and Knowledge Systems, Lecture Notes in Computer Science, Springer, 2002, 178-196 Maletic, J.

Space Decomposition In Data Mining: A Clustering Approach, Proceedings of the 14th International Symposium On Methodologies For Intelligent Systems, Maebashi, Japan, Lecture Notes in Computer Science, Springer-Verlag, 2003, pp. 24–31. Rokach, L. and Maimon, O. , Information Retrieval System for Medical Narrative Reports, Lecture Notes in Artificial intelligence 3055, page 217-228 SpringerVerlag, 2004. Rokach, L. and Maimon, O. , Selective voting-getting more for less in sensor fusion, International Journal of Pattern Recognition and Artificial Intelligence 20 (3) (2006), pp.

2, is created as a result of this method. 2. Dataset with Deleted Cases with Missing Attribute Values. 2. However, there are some reasons (Allison, 2002), (Little and Rubin, 2002) to consider it a good method. 2 The Most Common Value of an Attribute In this method, one of the simplest methods to handle missing attribute values, such values are replaced by the most common value of the attribute. In different words, a missing attribute value is replaced by the most probable known attribute value, where such probabilities are represented by relative frequencies of corresponding attribute values.

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Data Mining and Knowledge Discovery Handbook (Springer series in solid-state sciences) by Oded Maimon, Lior Rokach


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