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A Comparative Analysis of Different Categorical Data Clustering Ensemble Methods in Data Mining

by S. Sarumathi, N. Shanthi, M. Sharmila
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 4
Year of Publication: 2013
Authors: S. Sarumathi, N. Shanthi, M. Sharmila
10.5120/14004-2050

S. Sarumathi, N. Shanthi, M. Sharmila . A Comparative Analysis of Different Categorical Data Clustering Ensemble Methods in Data Mining. International Journal of Computer Applications. 81, 4 ( November 2013), 46-55. DOI=10.5120/14004-2050

@article{ 10.5120/14004-2050,
author = { S. Sarumathi, N. Shanthi, M. Sharmila },
title = { A Comparative Analysis of Different Categorical Data Clustering Ensemble Methods in Data Mining },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 4 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 46-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number4/14004-2050/ },
doi = { 10.5120/14004-2050 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:55:14.073736+05:30
%A S. Sarumathi
%A N. Shanthi
%A M. Sharmila
%T A Comparative Analysis of Different Categorical Data Clustering Ensemble Methods in Data Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 4
%P 46-55
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Over the past decades, a prevalent amount of work has been done in the data clustering research under the unsupervised learning technique in Data mining. Moreover a myriad of algorithms and methods has been proposed focusing on clustering different data types, representation of cluster models, and accuracy rates of the clusters. However no single clustering algorithm proves to be the most efficient in providing best results. Accordingly in order to find the solution to this issue a new technique, called Cluster ensemble method was bloomed. This cluster ensemble is a good alternative approach for facing the cluster analysis problem. The main aspire of the cluster ensemble is to combine different clustering solutions in such a way to achieve accuracy and to improve the quality of individual data clustering. Due to the substantial and unremitting development of the new methods in the sphere of data mining, it is obligatory to make a critical analysis of the existing techniques and the future novelty. This paper reveals the comparative study of different cluster ensemble methods along with their features, systematic working process and the average accuracy and error rates of each ensemble methods. Consequently this theoretical and comprehensive analysis will be very useful for the community of clustering practitioners and also helps in deciding the most suitable one to rectify the problem in hand.

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Index Terms

Computer Science
Information Sciences

Keywords

Cluster Ensemble methods Co-association matrix Consensus function Median partition.

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