Fuzzy C Means Clustering Algorithm for High Dimensional Data Using Feature Subset Selection Technique
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 2
Abstract
Feature choice involves characteristic a set of the foremost helpful options that produces compatible results because the original entire set of options. A feature choice rule is also evaluated from each the potency and effectiveness points of read. Whereas the potency considerations the time needed to search out a set of options, the effectiveness is expounded to the standard of the set of options. Supported these criteria, an economical Fuzzy C Means (FCM) is projected and by experimentation evaluated in this paper. The quick rule works in 2 steps. Within the commencement, options area unit divided into clusters by exploitation graph-theoretic cluster ways. Within the second step, the foremost representative feature that's powerfully associated with target categories is chosen from every cluster to create a set of options. Options in numerous clusters area unit comparatively freelance; the clustering-based strategy of quick incorporates a high chance of manufacturing a set of helpful and independent options. To make sure the potency of quick, we have a tendency to adopt the economical Fuzzy C Means (FCM) cluster technique.The potency associated effectiveness of the quick rule area unit evaluated through an empirical study. in depth experiments area unit dole out to match quick and a number of other representative feature choice algorithms, namely, FCBF, ReliefF, CFS, Consist, and FOCUS-SF, with relevancy four kinds of well
Authors and Affiliations
N. Manjula , S. Pandiarajan , J. Jagadeesan
Keywords
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- EP ID EP99693
- DOI 10.9790/0661-16226469
- Views 88
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