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Reseach Article

A Comprehensive Study of Challenges and Approaches for Clustering High Dimensional Data

by Neelam Singh, Neha Garg, Janmejay Pant
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 92 - Number 4
Year of Publication: 2014
Authors: Neelam Singh, Neha Garg, Janmejay Pant
10.5120/15995-4844

Neelam Singh, Neha Garg, Janmejay Pant . A Comprehensive Study of Challenges and Approaches for Clustering High Dimensional Data. International Journal of Computer Applications. 92, 4 ( April 2014), 7-10. DOI=10.5120/15995-4844

@article{ 10.5120/15995-4844,
author = { Neelam Singh, Neha Garg, Janmejay Pant },
title = { A Comprehensive Study of Challenges and Approaches for Clustering High Dimensional Data },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 92 },
number = { 4 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 7-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume92/number4/15995-4844/ },
doi = { 10.5120/15995-4844 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:13:23.962996+05:30
%A Neelam Singh
%A Neha Garg
%A Janmejay Pant
%T A Comprehensive Study of Challenges and Approaches for Clustering High Dimensional Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 92
%N 4
%P 7-10
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Clustering is one of the most effective methods for summarizing and analyzing datasets that are collection of data objects similar or dissimilar in nature. Clustering aims at finding groups, or clusters, of objects with similar attributes. Most clustering methods work efficiently for low dimensional data since distance measures are used to find dissimilarities between objects. High dimensional data, however, may contain attributes which are not required for defining clusters and irrelevant dimension may produce noise and will hide the clusters that are required to be created. The discovery of groups of objects that are highly similar within some subsets of relevant attributes becomes an important but challenging task. In this paper we provide a short introduction to various approaches and challenges for high-dimensional data clustering.

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Index Terms

Computer Science
Information Sciences

Keywords

Clustering high dimensional data summarizing analyzing clusters