Open AccessCOMPARATIVE STUDY OF DISTANCE MEASURES ON FUZZY SUBTRACTIVE CLUSTERING
Author(s) -
Anisa Eka Haryati,
Sugiyarto Surono
Publication year - 2021
Publication title -
media statistika
Language(s) - English
Resource type - Journals
ISSN - 2477-0647
DOI - 10.14710/medstat.14.2.137-145
Subject(s) - subtractive color , mathematics , cluster analysis , fuzzy clustering , hamming distance , k medians clustering , minkowski distance , pattern recognition (psychology) , fuzzy logic , data mining , artificial intelligence , cure data clustering algorithm , algorithm , statistics , computer science , physics , euclidean distance , geometry , optics
Clustering is a data analysis process which applied to classify the unlabeled data. Fuzzy clustering is a clustering method based on membership value which enclosing set of fuzzy as a measurement base for classification process. Fuzzy Subtractive Clustering (FSC) is included in one of fuzzy clustering method. This research applies Hamming distance and combined Minkowski Chebysev distance as a distance parameter in Fuzzy Subtractive Clustering. The objective of this research is to compare the output quality of the cluster from Fuzzy Subtractive Clustering by using Hamming distance and combine Minkowski Chebysev distance. The comparison of the two distances aims to see how well the clusters are produced from two different distances. The data used is data on hypertension. The variables used are age, gender, systolic pressure, diastolic pressure, and body weight. This research shows that the Partition Coefficient value resulted on Fuzzy Subtractive Clustering by applying combined Minkowski Chebysev distance is higher than the application of Hamming distance. Based on this, it can be concluded that in this study the quality of the cluster output using the combined Minkowski Chebysev distance is better.