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All Journal Jurnal Ilmiah Kursor
Rizki Desi Arindra Putri
cDepartment of Mathematics, Universitas Ahmad Dahlan
Computer Science & IT Decision Sciences, Operations Research & Management

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COMPARISON OF FUZZY SUBTRACTIVE CLUSTERING AND FUZZYC-MEANS Annisa Eka Haryati; Sugiyarto Sugiyarto; Rizki Desi Arindra Putri
Jurnal Ilmiah Kursor Vol 11 No 1 (2021)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i1.254

Abstract

Multivariate statistics have related problems with large data dimensions. One method that can be used is principal component analysis (PCA). Principal component analysis (PCA) is a technique used to reduce data dimensions consisting of several dependent variables while maintaining variance in the data. PCA can be used to stabilize measurements in statistical analysis, one of which is cluster analysis. Fuzzy clustering is a method of grouping based on membership values ​​that includes fuzzy sets as a weighting basis for grouping. In this study, the fuzzy clustering method used is Fuzzy Subtractive Clustering (FSC) and Fuzzy C-Means (FCM) with a combination of the Minkowski Chebysev distance. The purpose of this study was to compare the cluster results obtained from the FSC and FCM using the DBI validity index. The results obtained indicate that the results of clustering using FCM are better than the FSC.
Co-Authors Annisa Eka Haryati Sugiyarto Sugiyarto