Feature selection and the concept of immediate neighborhood in the context of clustering techniques

Dasarathy, BV (1974) Feature selection and the concept of immediate neighborhood in the context of clustering techniques. In: Proceedings of the IEEE, 62 (4). pp. 529-530.

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Abstract

The concept of feature selection in a nonparametric unsupervised learning environment is practically undeveloped because no true measure for the effectiveness of a feature exists in such an environment. The lack of a feature selection phase preceding the clustering process seriously affects the reliability of such learning. New concepts such as significant features, level of significance of features, and immediate neighborhood are introduced which result in meeting implicitly the need for feature slection in the context of clustering techniques.

Item Type:	Journal Article
Publication:	Proceedings of the IEEE
Publisher:	IEEE
Additional Information:	Copyright 1974 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	17 Dec 2009 10:06
Last Modified:	05 Mar 2019 06:19
URI:	http://eprints.iisc.ac.in/id/eprint/23915

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