Rough Sets Fuzzy Sets And Knowledge Discovery
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Rough Sets, Fuzzy Sets and Knowledge Discovery
Author: Wojciech P. Ziarko
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
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 in Knowledge Discovery 2
Author: Lech Polkowski
language: en
Publisher: Boom Koninklijke Uitgevers
Release Date: 1998-08-20
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 determines the importance of these techniques for the rapidly growing field of knowledge discovery. Volume 1 and 2 will bring together articles covering the present state of the methods developed in this field of research. Among the topics covered we may mention: rough mereology and rough mereological approach to knowledge discovery in distributed systems; discretization and quantization of attributes; morphological aspects of rough set theory; analysis of default rules in the framework of rough set theory.
Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications
The book will provide: 1) In depth explanation of rough set theory along with examples of the concepts. 2) Detailed discussion on idea of feature selection. 3) Details of various representative and state of the art feature selection techniques along with algorithmic explanations. 4) Critical review of state of the art rough set based feature selection methods covering strength and weaknesses of each. 5) In depth investigation of various application areas using rough set based feature selection. 6) Complete Library of Rough Set APIs along with complexity analysis and detailed manual of using APIs 7) Program files of various representative Feature Selection algorithms along with explanation of each. The book will be a complete and self-sufficient source both for primary and secondary audience. Starting from basic concepts to state-of-the art implementation, it will be a constant source of help both for practitioners and researchers. Book will provide in-depth explanation of concepts supplemented with working examples to help in practical implementation. As far as practical implementation is concerned, the researcher/practitioner can fully concentrate on his/her own work without any concern towards implementation of basic RST functionality. Providing complexity analysis along with full working programs will further simplify analysis and comparison of algorithms.