Open AccessClustering with Principal Component Analysis and Fuzzy Subtractive Clustering Using Membership Function Exponential and Hamming Distance
Author(s) -
Annisa Eka Haryati,
Sugiyarto
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1077/1/012019
Subject(s) - principal component analysis , cluster analysis , fuzzy clustering , mathematics , pattern recognition (psychology) , data mining , k medians clustering , dimensionality reduction , artificial intelligence , hierarchical clustering , fuzzy logic , computer science , cure data clustering algorithm , statistics
The problem of dimension reduction in multivariate data is how to obtain a smaller number of variables but still be able to maintain most of the information contained in the data. One method that can be used is principal component analysis (PCA). Principal component analysis (PCA) is a technique used to reduce the dimensions of data consisting of several dependent variables while maintaining the 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 include fuzzy sets as a basis for weighting for grouping. One method of fuzzy clustering is Fuzzy Subtractive Clustering (FSC). The method used in this study is PCA and FSC. The purpose of this study is to compare the most optimal cluster results using PCAFSC and FSC methods. The results obtained indicate that the clustering using the PCAFSC method is better than the FSC method.