C ONSTRAINT BASED P ARTITIONAL C LUSTERING – A C OMPREHENSIVE S TUDY AND A NALYSIS Aparna

Data clustering is the concept of forming predefined number of clusters where the data points within each cluster are very similar to each other and the data points between clusters are dissimilar to each other. The concept of clustering is widely used in various domains like bioinformatics, medical data, imaging, marketing study and crime analysis. The popular types of clustering techniques are partitional, hierarchical, spectral, density-based, mixture-modelling etc. Partitional clustering is a widely used technique for most of the applications since it is computationally inexpensive. An analysis of the various research works available on partitional clustering gives an insight into the recent problems in partitional clustering domain. In this paper, nine research articles from 2005 to 2013 have been taken for survey in order to analyse the different concepts used for constrained based partitional clustering techniques. Also, a comparative analysis is carried out, to find out the importance of the different approaches that can be adopted, so that any new development in constrained based partitional data clustering can be made easier to be carried out by researchers.