• Annisa Eka Haryati Department of Masters in Mathematics Education, Universitas Ahmad Dahlan
  • Sugiyarto Sugiyarto cDepartment of Mathematics, Universitas Ahmad Dahlan
  • Rizki Desi Arindra Putri cDepartment of Mathematics, Universitas Ahmad Dahlan


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.


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How to Cite
HARYATI, Annisa Eka; SUGIYARTO, Sugiyarto; PUTRI, Rizki Desi Arindra. COMPARISON OF FUZZY SUBTRACTIVE CLUSTERING AND FUZZYC-MEANS. Jurnal Ilmiah Kursor, [S.l.], v. 11, n. 1, july 2021. ISSN 2301-6914. Available at: <>. Date accessed: 22 jan. 2022. doi: