Feature Selection for Knowledge Discovery and Data Mining

Overview

With advanced computer technologies and their omnipresent usage, data accumulates in a speed unmatchable by the human's capacity to process data. To meet this growing challenge, the research community of knowledge discovery from databases emerged. The key issue studied by this community is, in layman's terms, to make advantageous use of large stores of data. In order to make raw data useful, it is necessary to represent, process, and extract knowledge for various applications.
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Paperback (Softcover reprint of the original 1st ed. 1998)
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Overview

With advanced computer technologies and their omnipresent usage, data accumulates in a speed unmatchable by the human's capacity to process data. To meet this growing challenge, the research community of knowledge discovery from databases emerged. The key issue studied by this community is, in layman's terms, to make advantageous use of large stores of data. In order to make raw data useful, it is necessary to represent, process, and extract knowledge for various applications.
Feature Selection for Knowledge Discovery and Data Mining offers an overview of the methods developed since the 1970s and provides a general framework in order to examine these methods and categorize them. This book employs simple examples to show the essence of representative feature selection methods and compares them using data sets with combinations of intrinsic properties according to the objective of feature selection. In addition, the book suggests guidelines on how to use different methods under various circumstances and points out new challenges in this exciting area of research.
Feature Selection for Knowledge Discovery and Data Mining is intended to be used by researchers in machine learning, data mining, knowledge discovery and databases as a toolbox of relevant tools that help in solving large real-world problems. This book is also intended to serve as a reference book or secondary text for courses on machine learning, data mining, and databases.

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Editorial Reviews

Booknews
Overviews methods developed since the 1970s for representing, processing, and extracting knowledge for various applications from the increasingly vast accumulation of data made possible, perhaps inevitable, by the spread of computers, and constructs a general framework within which to examine and categorize the methods. Presents simple examples to show the essence of representative feature- selection methods, compares them using data sets with combinations of intrinsic properties according to the objective of selecting features, suggests guidelines for using different methods under various circumstances, and identifies new challenges to research in the field. A reference for researchers in machine learning, data mining, knowledge discover, or databases, or a supplementary text for courses on those areas. Annotation c. by Book News, Inc., Portland, Or.
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Product Details

Table of Contents

List of Figures. List of Tables. Preface. 1. Data Processing and KDD. 2. Perspectives of Feature Selection. 3. Aspects of Feature Selection. 4. Feature Selection Methods. 5. Evaluation and Application. 6. Feature Transformation and Dimensionality Reduction. 7. Less is More. Appendices: A. Data Mining and Knowledge Discovery Sources. B. Data Sets and Software Used in This Book. Index.

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