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Effective K-Means Document Clustering using Dictionary Defined Lexical Analyzer (DDLA)

by R. Ranga Raj, M. Punithavalli
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 59 - Number 20
Year of Publication: 2012
Authors: R. Ranga Raj, M. Punithavalli
10.5120/9816-4363

R. Ranga Raj, M. Punithavalli . Effective K-Means Document Clustering using Dictionary Defined Lexical Analyzer (DDLA). International Journal of Computer Applications. 59, 20 ( December 2012), 4-8. DOI=10.5120/9816-4363

@article{ 10.5120/9816-4363,
author = { R. Ranga Raj, M. Punithavalli },
title = { Effective K-Means Document Clustering using Dictionary Defined Lexical Analyzer (DDLA) },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 59 },
number = { 20 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 4-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume59/number20/9816-4363/ },
doi = { 10.5120/9816-4363 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:04:45.874403+05:30
%A R. Ranga Raj
%A M. Punithavalli
%T Effective K-Means Document Clustering using Dictionary Defined Lexical Analyzer (DDLA)
%J International Journal of Computer Applications
%@ 0975-8887
%V 59
%N 20
%P 4-8
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Due to tremendous increase in number of documents, clustering of such document is difficult one. Document Clustering is the process of grouping related documents from the large collection of database. The mining of such related documents from the database which are unlabelled is a challenging one. To overcome this process, clustering is used to filter the unlabelled documents from the large collection of database. In this paper, a new concept is introduced for the document clustering by using k-means Enhanced Approach algorithm [1] with the Dictionary Defined Lexical Analyzer (DDLA). Basically K-Mean algorithm clusters the numeric values efficiently. But with the inclusion of DDLA the characters, words and sentences can also be clustered. Based on the weights, documents are clustered [7] by using bisecting k-means algorithm [1, 2] and topic detection method. The discovery of meaningful labels for the document is based on semantic similarity [8]. The efficient clustering of unlabeled documents with enhanced K-Mean algorithm and DDLA is one of the techniques which make clustering in an easiest way.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Clustering K-Means Enhanced Approach Algorithm Lexical Analyzer Defined Dictionary DDLA.

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