Research Article

Cluster Analysis: An Experimental Study

by  Sathya Ramadass, Annamma Abraham
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 64 - Issue 14
Published: February 2013
Authors: Sathya Ramadass, Annamma Abraham
10.5120/10704-5622
PDF

Sathya Ramadass, Annamma Abraham . Cluster Analysis: An Experimental Study. International Journal of Computer Applications. 64, 14 (February 2013), 32-36. DOI=10.5120/10704-5622

                        @article{ 10.5120/10704-5622,
                        author  = { Sathya Ramadass,Annamma Abraham },
                        title   = { Cluster Analysis: An Experimental Study },
                        journal = { International Journal of Computer Applications },
                        year    = { 2013 },
                        volume  = { 64 },
                        number  = { 14 },
                        pages   = { 32-36 },
                        doi     = { 10.5120/10704-5622 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2013
                        %A Sathya Ramadass
                        %A Annamma Abraham
                        %T Cluster Analysis: An Experimental Study%T 
                        %J International Journal of Computer Applications
                        %V 64
                        %N 14
                        %P 32-36
                        %R 10.5120/10704-5622
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

Clustering plays a vital role in machine based learning algorithms and in the present study, it is found that, the competitive learning algorithm that is very efficient for a number of non-linear real-time problems, offers efficient solution for clustering. This paper presents a comparative account of self-organizing models and proposes a hybrid self-organizing model for cluster analysis. The potential usefulness of cluster analysis for higher education scenario is taken to study in this paper.

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Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

ART Classification Clustering HSOM SOM Supervised learning Unsupervised learning

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