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 (386 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

GET 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
International Symposium on Fuzzy Systems, Knowledge Discovery and Natural Computation (FSKD 2014)
Language: en
Pages: 657
Authors: Defu Zhang, Xiamen University, China
Categories: Language Arts & Disciplines
Type: BOOK - Published: 2014-09-02 - Publisher: DEStech Publications, Inc

GET EBOOK

ICNC-FSKD is a premier international forum for scientists and researchers to present the state of the art of data mining and intelligent methods inspired from n
Fuzzy Systems and Data Mining VII
Language: en
Pages: 494
Authors: C. Shen
Categories: Computers
Type: BOOK - Published: 2021-11-04 - Publisher: IOS Press

GET EBOOK

Fuzzy systems and data mining are indispensible aspects of the computer systems and algorithms on which the world has come to depend. This book presents papers
Advanced Fuzzy Systems Design and Applications
Language: en
Pages: 292
Authors: Yaochu Jin
Categories: Computers
Type: BOOK - Published: 2003 - Publisher: Springer Science & Business Media

GET EBOOK

This book presents a variety of recently developed methods for generating fuzzy rules from data with the help of neural networks and evolutionary algorithms. Sp
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Language: en
Pages: 581
Authors: Nikola K. Kasabov
Categories: Artificial intelligence
Type: BOOK - Published: 1996 - Publisher: Marcel Alencar

GET EBOOK

Combines the study of neural networks and fuzzy systems with symbolic artificial intelligence (AI) methods to build comprehensive AI systems. Describes major AI