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Reseach Article

Term Importance Degree Impact on Search Result Clustering

by Soheila Karbasi, Mehdi Yaghoubi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 89 - Number 2
Year of Publication: 2014
Authors: Soheila Karbasi, Mehdi Yaghoubi
10.5120/15475-4164

Soheila Karbasi, Mehdi Yaghoubi . Term Importance Degree Impact on Search Result Clustering. International Journal of Computer Applications. 89, 2 ( March 2014), 32-34. DOI=10.5120/15475-4164

@article{ 10.5120/15475-4164,
author = { Soheila Karbasi, Mehdi Yaghoubi },
title = { Term Importance Degree Impact on Search Result Clustering },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 89 },
number = { 2 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 32-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume89/number2/15475-4164/ },
doi = { 10.5120/15475-4164 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:08:13.318203+05:30
%A Soheila Karbasi
%A Mehdi Yaghoubi
%T Term Importance Degree Impact on Search Result Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 89
%N 2
%P 32-34
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

As wellactual clustering algorithms have to deal with explosive growth of documents of various sizes and terms of various frequencies, an appropriate term-weighting scheme has a crucial impact on the overall performance of such systems. Term-weighting is one of the critical process for document retrieval and ranking in most search result clustering systems. In this paper we introduce a new technique forclustering algorithms that solve the problem of indexing the terms of big datasets and their characteristicswhich exist in most of current clustering approaches. The paper focus on term frequency normalization step ofclustering algorithms. Anew factor has been applied tobasic term-weighting schemes for using in clustering process. The evaluated results confirm the impact of this factor to increase the performance of clusteringtechniques. The experiments were carried out on the standard algorithms and ODP-239 datasets which validated by statistical tests.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Weighted clustering Term importance degree Term frequency normalization