Rough Sets and Data Mining - Analysis of Imprecise Data (Paperback, Softcover reprint of the original 1st ed. 1997)

Rough Sets and Data Mining: Analysis of Imprecise Data is an edited collection of research chapters on the most recent developments in rough set theory and data mining. The chapters in this work cover a range of topics that focus on discovering dependencies among data, and reasoning about vague, uncertain and imprecise information. The authors of these chapters have been careful to include fundamental research with explanations as well as coverage of rough set tools that can be used for mining data bases. The contributing authors consist of some of the leading scholars in the fields of rough sets, data mining, machine learning and other areas of artificial intelligence. Among the list of contributors are Z. Pawlak, J Grzymala-Busse, K. Slowinski, and others. Rough Sets and Data Mining: Analysis of Imprecise Data will be a useful reference work for rough set researchers, data base designers and developers, and for researchers new to the areas of data mining and rough sets.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Release date:	October 2011
First published:	1997
Editors:	T. Y. Lin • N. Cercone
Dimensions:	235 x 155 x 23mm (L x W x T)
Format:	Paperback
Pages:	436
Edition:	Softcover reprint of the original 1st ed. 1997
ISBN-13:	978-1-4612-8637-0
Categories:	Books > Computing & IT > General theory of computing > Data structures Books > Computing & IT > Computer programming > Algorithms & procedures Books > Science & Mathematics > Mathematics > Mathematical foundations > General Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	1-4612-8637-9
Barcode:	9781461286370

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