Branches of evolutionary algorithms and their effectiveness for clustering records

Clustering is a process that aims to group the similar records in one cluster and dissimilar records in different clusters. K-means is one of the most popular and well-known clustering technique for its simplicity and light weight. However, the main drawback of K-means clustering technique is that it requires a user (data miner) to estimate the number of clusters in advance. Another limitation of K-means is that it has a tendency to get stuck at local optima. In order to overcome these limitations many evolutionary algorithm based clustering techniques have been proposed since the 1990s and applied to various fields. In this paper, we present an up-to-date review of some major evolutionary algorithm based clustering techniques for the last twenty (20) years (1995-2015). A total of 63 ranked (i.e. based on citation reports and JCR/CORE rank) evolutionary algorithm based clustering approaches are reviewed. Maximum of the techniques do not require any user to define the number of clusters in advance. We present the limitations and advantages of some evolutionary algorithm based clustering techniques. We also present a thorough discussion and future research directions of evolutionary algorithm based clustering techniques.