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Reseach Article

Hierarchical Document Clustering: A Review

Published on November 2011 by Ashish Jaiswal, Prof. Nitin Janwe
2nd National Conference on Information and Communication Technology
Foundation of Computer Science USA
NCICT - Number 3
November 2011
Authors: Ashish Jaiswal, Prof. Nitin Janwe
03233b8b-4cbc-4c24-afed-f0ba889ce7bd

Ashish Jaiswal, Prof. Nitin Janwe . Hierarchical Document Clustering: A Review. 2nd National Conference on Information and Communication Technology. NCICT, 3 (November 2011), 37-41.

@article{
author = { Ashish Jaiswal, Prof. Nitin Janwe },
title = { Hierarchical Document Clustering: A Review },
journal = { 2nd National Conference on Information and Communication Technology },
issue_date = { November 2011 },
volume = { NCICT },
number = { 3 },
month = { November },
year = { 2011 },
issn = 0975-8887,
pages = { 37-41 },
numpages = 5,
url = { /proceedings/ncict/number3/4294-ncict024/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 2nd National Conference on Information and Communication Technology
%A Ashish Jaiswal
%A Prof. Nitin Janwe
%T Hierarchical Document Clustering: A Review
%J 2nd National Conference on Information and Communication Technology
%@ 0975-8887
%V NCICT
%N 3
%P 37-41
%D 2011
%I International Journal of Computer Applications
Abstract

As text documents are largely increasing in the internet, the process of grouping similar documents for versatile applications have put the eye of researchers in this area. However most clustering methods suffer from challenges in dealing with problems of high dimensionality, scalability, accuracy and meaningful cluster labels. This paper presents a review on all these well known methods of document clustering. Hierarchical document clustering method is explained in detail. Study shows that hierarchical document clustering performs well but still there is a scope to improve above mentioned problems.

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Index Terms

Computer Science
Information Sciences

Keywords

Document clustering Hierarchical clustering Frequent item sets