PENERAPAN METODE ANT COLONY OPTIMZATION PADA METODE K-HARMONIC MEANS UNTUK KLASTERISASI DATA

Abstract

Data can be classified into several clusters, better known as Data Clustering using several methods, one of which is referred to as K-Means method (KM). It is one of the popular data clustering method. Its implementation is simple and can cope with a great number of data and the process is relatively short. However, KM has several weaknesses; the clustering result is sensitive to the initialization of the cluster center and leads to optimal local. It is the betterment of KM method referred to as K-Harmonic Means (KHM). Although it can minimize in the initialization, it could not overcome the problem of optimal local yet.Ant Colony Optimization (ACO) is an ant algorithm used to form a colony. ACO could avoid the problem of local optimal and was proved to have global solution. In this study, an algorithm was applied to clusterizing the ACO and KHM-based data referred to as ACOKHM. The performance of ACOKHM was compared to the algorithms of ACO and KHM using five data sets. The ACOKHM algorithm was proved to have better performance than ACO and KHM, in which ACOKHM could maximize the cluster center which directs to optimal global.