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A Modified Projected K-Means Clustering Algorithm with Effective Distance Measure

by B. Shanmugapriya, M. Punithavalli
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 8
Year of Publication: 2012
Authors: B. Shanmugapriya, M. Punithavalli
10.5120/6285-8468

B. Shanmugapriya, M. Punithavalli . A Modified Projected K-Means Clustering Algorithm with Effective Distance Measure. International Journal of Computer Applications. 44, 8 ( April 2012), 32-36. DOI=10.5120/6285-8468

@article{ 10.5120/6285-8468,
author = { B. Shanmugapriya, M. Punithavalli },
title = { A Modified Projected K-Means Clustering Algorithm with Effective Distance Measure },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 8 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 32-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number8/6285-8468/ },
doi = { 10.5120/6285-8468 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:35:01.988321+05:30
%A B. Shanmugapriya
%A M. Punithavalli
%T A Modified Projected K-Means Clustering Algorithm with Effective Distance Measure
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 8
%P 32-36
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Clustering high dimensional data has been a big issue for clustering algorithms because of the intrinsic sparsity of the data points. Several recent research results signifies that in case of high dimensional data, even the notion of proximity or clustering possibly will not be significant. K-Means is one of the basic clustering algorithm which is commonly used in several applications, but it is not possible to discover subspace clusters. The subspaces are explicit to the clusters themselves. In this paper, an algorithm called Modified Projected K-Means Clustering Algorithm with Effective Distance Measure is designed to generalize K-Means algorithm with the objective of managing the high dimensional data. The experimental results confirm that the proposed algorithm is an efficient algorithm with better clustering accuracy and very less execution time than the Standard K-Means and General K-Means algorithms.

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Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Projected Clustering K-means High Dimensional Data General K-means Efficient Projected Clustering (epc)

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