Open AccessCOMPARISON OF FUZZY SUBTRACTIVE CLUSTERING AND FUZZYC-MEANS
Author(s) -
Annisa Eka Haryati,
Sugiyarto Sugiyarto,
Rizki Desi Arindra Putri
Publication year - 2021
Publication title -
jurnal ilmiah kursor: menuju solusi teknologi informasi
Language(s) - English
Resource type - Journals
ISSN - 2301-6914
DOI - 10.21107/kursor.v11i1.254
Subject(s) - principal component analysis , cluster analysis , mathematics , fuzzy clustering , data mining , pattern recognition (psychology) , subtractive color , fuzzy logic , weighting , artificial intelligence , statistics , computer science , medicine , art , visual arts , radiology
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.