Fuzzy C Means Clustering Algorithm for High Dimensional Data Using Feature Subset Selection Technique
Year: 2014
Type: article
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 wellknown classifiers, namely, the chance primarily based Naive Thomas Bayes, the tree-based C4.5, the instancebased IB1,and also the rule-based liquidator before and once feature choice.The results, on thirty five in public on the market real-world high-dimensional image, microarray, and text knowledge, demonstrate that the quick not solely produces smaller subsets of options however additionally improves the performances of the four kinds of classifiers.
Authors: N. Manjula, S. Pandiarajan, J. Jagadeesan
Institution: SRM Institute of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2123377516