Cluster Validity Index to Determine the Optimal Number Clusters of Fuzzy Clustering for Classify Customer Buying Behavior

Abstract

One of the strategies in order to compete in Batik MSMEs is to look at the characteristics of the customer. To make it easier to see the characteristics of customer buying behavior, it is necessary to classify customers based on similarity of characteristics using fuzzy clustering. One of the parameters that must be determined at the beginning of the fuzzy clustering method is the number of clusters. Increasing the number of clusters does not guarantee the best performance, but the right number of clusters greatly affects the performance of fuzzy clustering. So to get optimal number cluster, we can measured the result of clustering in each number cluster using the cluster validity index. From several types of cluster validity index, NPC give the best value. Optimal number cluster that obtained by the validity index is 2 and this number cluster give classify result with small variance value

Identification of Public Library Visitor Profiles using K-means Algorithm based on The Cluster Validity Index

“…The method that can be used in the grouping process is K-means clustering (Amanda & Sitorus, 2021) (Sudrajat et al, 2022a), however, if the number of K-clusters is not correct, the results will not be optimal. To overcome this, it is necessary to test the K-Cluster using the Cluster Validity index (Widia et al, 2021). Therefore, in this study, the process of grouping visitor profiles uses the K-Menas Clustering method with the Cluster Validity Index.…”