Optimal k-means clustering using artificial bee colony algorithm with variable food sources length

Abstract

Clustering is a robust machine learning task that involves dividing data points into a set of groups with similar traits. One of the widely used methods in this regard is the k-means clustering algorithm due to its simplicity and effectiveness. However, this algorithm suffers from the problem of predicting the number and coordinates of the initial clustering centers. In this paper, a method based on the first artificial bee colony algorithm with variable-length individuals is proposed to overcome the limitations of the k-means algorithm. Therefore, the proposed technique will automatically predict the clusters number (the value of k) and determine the most suitable coordinates for the initial centers of clustering instead of manually presetting them. The results were encouraging compared with the traditional k-means algorithm on three real-life clustering datasets. The proposed algorithm outperforms the traditional k-means algorithm for all tested real-life datasets.