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HYBRID ANN AND FIREWORKS ALGORITHM FOR REAL-TIME SOFTWARE RELIABILITY PREDICTION

Ivy Botchway Felix Larbi Aryeh Boniface Kayode Alese
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Cluster Analysis: An Experimental Study

by Sathya Ramadass, Annamma Abraham
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 64 - Number 14
Year of Publication: 2013
Authors: Sathya Ramadass, Annamma Abraham
10.5120/10704-5622

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 },
issue_date = { February 2013 },
volume = { 64 },
number = { 14 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 32-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume64/number14/10704-5622/ },
doi = { 10.5120/10704-5622 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:16:27.786649+05:30
%A Sathya Ramadass
%A Annamma Abraham
%T Cluster Analysis: An Experimental Study
%J International Journal of Computer Applications
%@ 0975-8887
%V 64
%N 14
%P 32-36
%D 2013
%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.

References
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Index Terms

Computer Science
Information Sciences

Keywords

ART Classification Clustering HSOM SOM Supervised learning Unsupervised learning

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