Cluster analysis using a gradient evolution-based k-means algorithm
R.J. Kuo, Ferani Eva Zulvia · 2016
Cluster analysis is a very useful data analysis tool. It can reveal the hidden information stored inside a dataset. Therefore, many researches proposed different clustering algorithms. This paper intends to propose a gradient evolutionbased Ä-means algorithm. Ä-means algorithm is a well-known clustering algorithm. It offers a simple algorithm to divide the dataset into several clusters. Unfortunately, its results are highly influenced by the initial centroids. Unpromising initial centroid might lead the k-means to the bad clustering result. This paper aims to improve this drawback by adopting a new metaheuristic algorithm, named a gradient evolution (GE) algorithm. In this paper, we proposed a GE-based Ä-means algorithm for solving the clustering problems. The proposed algorithm is validated by using some benchmark datasets. The computation results showed that the proposed algorithm can obtain better results compared with some other metaheuristic-based k-means algorithms.