Comparison of Clustering Techniques for Thai Mutual Funds Fee Dataset

Abstract

There are researches that study about clustering techniques e.g., K-Means, K-Medoids, and X-Means. Their works mainly focus on applying one technique on multiple data sets to find the pros and cons of each algorithm. In this work, we focus on study and comparing these three clustering techniques instead. The experiment is done by applying each technique on Thai mutual funds fee data set which consists of 2,595 funds. From our experiment, we found that the optimal K value is 22. K-Means use the least processing time while K-Medoids use the most time. K-Means also has the least average distant between each centroid while K-Medoids has the most average distant. From Davies-Bouldin index, X-Means has the lowest value while K-Medoids has the highest value. The most density cluster of K-Means and X-Means is cluster 0 but it is cluster 1 for K-Medoids.