Comprehensive Study and Analysis of Partitional Data Clustering Techniques

Abstract

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.

Grouping of E Learners Using Fuzzy K-Medoid Clustering

“…Mostly mean and median is used as a cluster centre to represent each cluster. This method is suitable for medium and small size of data set [8]. Pros 1.…”

Determination of the Representative Socioeconomic Level by BSA in the Mexican Republic

“…Objects that have similarities in their characteristics tend to belong to the same group, whereas those with different characteristics tend to belong to different groups (Krawczyk, 2016). In addition, it is a useful exploratory method when it comes to solving classification and segmentation problems (Aghdaie et al, 2013;Aparna & Nair, 2015).…”

Supervised Regression Clustering

“…While the methods underlying conventional clustering approaches (Ackerman, & Ben-David, 2009;Aparna, & Mydhili, 2015;Bach, & Jordan, 2003;Bouveyron, Girard, & Schmid, 2007;Bock, 1996;Cardot, Cenac, & Monnez, 2012;Cuevas, Febrero, & Fraiman, 2001;Dhillon, Guan, & Kogan, 2002;Hruschka, & Natter, 1999;Jain, & Dubes, 1988;Klein, Kamvar, & Manning, 2002;Mulvey, & Beck, 1984;Sarkar, Yegnanarayana, & Khemani, 1997) have been studied almost comprehensively, methods in supervised clustering (Awasthi, & Bosagh Zadeh, 2010;Grira, Crucianu, Boujemaa, 2004) have been studied far less deeply and are typically addressed under semi-supervised clustering (Gao,, Tan, & Cheng, 2006;Hochbaum, & Shmoys, 1985;Xing, Jordan, Russell, & Ng, 2003;Wagstaff, Cardie, Rogers, & Schrödl, 2001). Seen from this view more investigation in this regard is desirable.…”