Selection of 'K' in K-means clustering using GA and VMA

Abstract

The K-means algorithm is the most widely used partitional clustering algorithms. In spite of several advances in K-means clustering algorithm, it suffers in some drawbacks like initial cluster centres, stuck in local optima etc. The initial guessing of cluster centres lead to the bad clustering results in K-means and this is one of the major drawbacks of K-means algorithm. In this paper, a new strategy is proposed where we have blended K-means algorithm with genetic algorithm (GA) and volume metric algorithm (VMA) to predict the best value of initial cluster centres, which is not in the case of only K-means algorithm. The paper concludes with the analysis of the results of using the proposed measure to determine the number of clusters for the K-means algorithm for different well-known datasets from UCI machine learning repository.