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Towards Unsupervised and Consistent High Dimensional Data Clustering

by R. G. Mehta, N. J. Mistry, M. Raghuwanshi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 87 - Number 2
Year of Publication: 2014
Authors: R. G. Mehta, N. J. Mistry, M. Raghuwanshi
10.5120/15183-3532

R. G. Mehta, N. J. Mistry, M. Raghuwanshi . Towards Unsupervised and Consistent High Dimensional Data Clustering. International Journal of Computer Applications. 87, 2 ( February 2014), 40-44. DOI=10.5120/15183-3532

@article{ 10.5120/15183-3532,
author = { R. G. Mehta, N. J. Mistry, M. Raghuwanshi },
title = { Towards Unsupervised and Consistent High Dimensional Data Clustering },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 87 },
number = { 2 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 40-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume87/number2/15183-3532/ },
doi = { 10.5120/15183-3532 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:04:55.301708+05:30
%A R. G. Mehta
%A N. J. Mistry
%A M. Raghuwanshi
%T Towards Unsupervised and Consistent High Dimensional Data Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 87
%N 2
%P 40-44
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The boosted demand for immense information, the enhanced data acquisition and so do the size and number of dimensions of data is a big challenge for the data mining algorithms. Clustering exercise to collect the data with same characteristics together, for better performance of knowledge based systems. High dimensional and large size data results in declined performance of existing clustering algorithms. PROCLUS is an efficient high dimensional clustering algorithm; consist of significant issues like inconsistency in results and expert supervised subspaces. MPROCLUS: a modified PROCLUS algorithm is proposed, aimed at improving the running time and consistency as well as the unsupervised selection of the parameter like, average number of dimensions. The promising and consistent results of MPROCLUS has open the sky wide open for further research for usage of MPROCLUS in stream Data Mining.

References
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Index Terms

Computer Science
Information Sciences

Keywords

High dimensional clustering Unsupervised and consistent clustering PROCLUS

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