Rough Sets and Data Mining: Analysis of Imprecise Data

Rough Sets and Data Mining: Analysis of Imprecise Data

by T.Y. Lin
     
 
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

Overview

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.

Product Details

ISBN-13:
9781461286370
Publisher:
Springer US
Publication date:
07/31/2012
Edition description:
Softcover reprint of the original 1st ed. 1997
Pages:
436
Product dimensions:
6.10(w) x 9.25(h) x 0.04(d)

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