Comprehensive Study and Analysis of Partitional Data Clustering Techniques

Comprehensive Study and Analysis of Partitional Data Clustering Techniques

Aparna K., Mydhili K. Nair
Copyright: © 2015 |Pages: 16
DOI: 10.4018/ijban.2015010102
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Data clustering has found significant applications in various domains like bioinformatics, medical data, imaging, marketing study and crime analysis. There are several types of data clustering such as partitional, hierarchical, spectral, density-based, mixture-modeling to name a few. Among these, partitional clustering is well suited for most of the applications due to the less computational requirement. An analysis of various literatures available on partitional clustering will not only provide good knowledge, but will also lead to find the recent problems in partitional clustering domain. Accordingly, it is planned to do a comprehensive study with the literature of partitional data clustering techniques. In this paper, thirty three research articles have been taken for survey from the standard publishers from 2005 to 2013 under two different aspects namely the technical aspect and the application aspect. The technical aspect is further classified based on partitional clustering, constraint-based partitional clustering and evolutionary programming-based clustering techniques. Furthermore, an analysis is carried out, to find out the importance of the different approaches that can be adopted, so that any new development in partitional data clustering can be made easier to be carried out by researchers.
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2. Literature Survey Of Different Partitional Data Clustering Algorithms

The survey of partitional data clustering algorithms is conducted with respect to two different aspects – the technical aspect and the application aspect as shown in Figure 1 in the Appendix. The survey proceeds further as follows:

Figure 1.

Survey of partitional clustering algorithms


2.1. Technical Aspect

  • K-Means Algorithm

  • Other Partitional Clustering algorithms

  • Constraint based Clustering

  • General Clustering Algorithm through Evolutionary Programming

2.2. Application Aspect

  • Applications based on Constraint based Clustering

  • Application oriented Clustering algorithm through Evolutionary Programming

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