DocumentCode :
1625993
Title :
Correlative analysis of soft clustering algorithms
Author :
Rajathi, S. ; Shajunisha, N. ; Caroline, S. Shiny
Author_Institution :
Dept. of Inf. Technol., Sathyabama Univ., Chennai, India
fYear :
2013
Firstpage :
360
Lastpage :
365
Abstract :
Clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Clustering can be considered one of the most important unsupervised learning techniques so as every other problem of this kind; it deals with finding a structure in a collection of unlabelled data. Clustering is of soft and hard clustering. Hard clustering refers to basic partitioning algorithms where object belongs to only one cluster. Soft clustering refers to data objects belonging to more than one cluster based on its membership values. This paper reviews three types of Soft clustering techniques: Fuzzy C-Mean, Rough C-Mean, and Rough Fuzzy C-Mean. Thereby calculating cluster validity indices for a synthetic dataset and a real dataset on applying these algorithms and ensuring best soft clustering algorithm through experimental analysis.
Keywords :
fuzzy set theory; pattern clustering; rough set theory; unsupervised learning; cluster validity index; correlative analysis; data collection; data objects; fuzzy c-mean; hard clustering algorithms; partitioning algorithms; rough c-mean; rough fuzzy c-mean; soft clustering algorithms; synthetic dataset; unsupervised learning techniques; Clustering algorithms; Indexes; Prototypes; Cluster Validity; Clustering; Soft Clustering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Computing (ICoAC), 2013 Fifth International Conference on
Conference_Location :
Chennai
Print_ISBN :
978-1-4799-3447-8
Type :
conf
DOI :
10.1109/ICoAC.2013.6921977
Filename :
6921977
Link To Document :
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