Rough Sets, Fuzzy Sets and Knowledge Discovery

Rough Sets, Fuzzy Sets and Knowledge Discovery
Author :
Publisher : Springer Science & Business Media
Total Pages : 486
Release :
ISBN-10 : 9781447132387
ISBN-13 : 1447132386
Rating : 4/5 (87 Downloads)

Book Synopsis Rough Sets, Fuzzy Sets and Knowledge Discovery by : Wojciech P. Ziarko

Download or read book Rough Sets, Fuzzy Sets and Knowledge Discovery written by Wojciech P. Ziarko and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this book is two-fold. Firstly, it is aimed at bringing to gether key research articles concerned with methodologies for knowledge discovery in databases and their applications. Secondly, it also contains articles discussing fundamentals of rough sets and their relationship to fuzzy sets, machine learning, management of uncertainty and systems of logic for formal reasoning about knowledge. Applications of rough sets in different areas such as medicine, logic design, image processing and expert systems are also represented. The articles included in the book are based on selected papers presented at the International Workshop on Rough Sets and Knowledge Discovery held in Banff, Canada in 1993. The primary methodological approach emphasized in the book is the mathematical theory of rough sets, a relatively new branch of mathematics concerned with the modeling and analysis of classification problems with imprecise, uncertain, or incomplete information. The methods of the theory of rough sets have applications in many sub-areas of artificial intelligence including knowledge discovery, machine learning, formal reasoning in the presence of uncertainty, knowledge acquisition, and others. This spectrum of applications is reflected in this book where articles, although centered around knowledge discovery problems, touch a number of related issues. The book is intended to provide an important reference material for students, researchers, and developers working in the areas of knowledge discovery, machine learning, reasoning with uncertainty, adaptive expert systems, and pattern classification.


Rough Sets, Fuzzy Sets and Knowledge Discovery Related Books

Rough Sets, Fuzzy Sets and Knowledge Discovery
Language: en
Pages: 486
Authors: Wojciech P. Ziarko
Categories: Computers
Type: BOOK - Published: 2012-12-06 - Publisher: Springer Science & Business Media

DOWNLOAD EBOOK

The objective of this book is two-fold. Firstly, it is aimed at bringing to gether key research articles concerned with methodologies for knowledge discovery in
Rough Sets in Knowledge Discovery 2
Language: en
Pages: 616
Authors: Lech Polkowski
Categories: Business & Economics
Type: BOOK - Published: 1998-08-20 - Publisher: Boom Koninklijke Uitgevers

DOWNLOAD EBOOK

The ideas and techniques worked out in Rough Set Theory allow for knowledge reduction and to finding near - to - functional dependencies in data. This fact dete
Rough – Granular Computing in Knowledge Discovery and Data Mining
Language: en
Pages: 162
Authors: J. Stepaniuk
Categories: Computers
Type: BOOK - Published: 2009-01-29 - Publisher: Springer

DOWNLOAD EBOOK

This book covers methods based on a combination of granular computing, rough sets, and knowledge discovery in data mining (KDD). The discussion of KDD foundatio
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing
Language: en
Pages: 764
Authors: Dominik Slezak
Categories: Computers
Type: BOOK - Published: 2005-09-19 - Publisher: Springer

DOWNLOAD EBOOK

This volume contains the papers selected for presentation at the 10th Int- national Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, R
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing
Language: en
Pages: 758
Authors: Guoyin Wang
Categories: Computers
Type: BOOK - Published: 2003-05-08 - Publisher: Springer Science & Business Media

DOWNLOAD EBOOK

This book constitutes the refereed proceedings of the 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2003,