Aplication of Validity Index in K Means and Fuzzy C Means

Abstract

K-Means and Fuzzy C-Means Clustering is a method of analyzing data that performs the modeling process without supervision (without supervision) and is a method that groups data by partitioning the system. Clusters Clusters and Fuzzy C-Means will produce different clusters in the same dataset, cluster validity index is a method that can be used to improve the results of clustering generated by the clustering method. This study will use the cluster validity index on the kmeans clustering algorithm and Fuzzy C-Means by calculating the index of validity of each kmeans clustering result with k = 2, ..., kmax (k max determined at the beginning) and the results from Fuzzy C-Means with c = 2, ...., cmax (c max is specified at the beginning). By using the cluster validity index, the most optimal cluster is obtained in the second cluster with the Dbi value = 0.45 in the mean K and the second cluster with the Dbi value = 0.5 in the Fuzzy C Mean, and the results of the clustering are consistent.