A Survey on Balanced Data Clustering Algorithms

A R T I C L E I N F O A B S T R A C T Article history: Received 25 March 2017 Received in revised form 27 March 2017 Accepted 29 March 2017 Available online 31 March 2017 In an era of data increasing at a proportion larger than ever, analysis of such data covering all businesses and sectors becomes all the more important. Data mining tasks, particularly clustering, aim at partitioning bulks of data into similar groups/clusters such that data points belonging to one cluster are semantically different from the data points belonging to different clusters. While the clustering algorithms proposed till date for enabling data searching, sorting, analysis, updation and more are numerous; the recent consideration is to achieve balance in clusters. Balance can be achieved in terms of size, importance, density and many other aspects. Balance also enhances the clustering performance of datasets. This paper surveys some of the popular balanced clustering algorithms.