Feature selection for knowledge discovery and data mining
Author(s)
Bibliographic Information
Feature selection for knowledge discovery and data mining
（The Kluwer international series in engineering and computer science, SECS 454）
Kluwer Academic Publishers, c1998
Available at / 22 libraries
-
No Libraries matched.
- Remove all filters.
Note
Includes bibliographical references and index
Description and Table of Contents
Description
As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The com puter generated data should be analyzed by computers; without the aid of computing technologies, it is certain that huge amounts of data collected will not ever be examined, let alone be used to our advantages. Even with today's advanced computer technologies (e. g. , machine learning and data mining sys tems), discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Taking its simplest form, raw data are represented in feature-values. The size of a dataset can be measUJ*ed in two dimensions, number of features (N) and number of instances (P). Both Nand P can be enormously large. This enormity may cause serious problems to many data mining systems. Feature selection is one of the long existing methods that deal with these problems. Its objective is to select a minimal subset of features according to some reasonable criteria so that the original task can be achieved equally well, if not better. By choosing a minimal subset offeatures, irrelevant and redundant features are removed according to the criterion. When N is reduced, the data space shrinks and in a sense, the data set is now a better representative of the whole data population. If necessary, the reduction of N can also give rise to the reduction of P by eliminating duplicates.
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.
by "Nielsen BookData"