NDLI: Enhancing GSOM text clustering with Latent Semantic Analysis

Content Provider	IEEE Xplore Digital Library
Author	Matharage, S. Alahakoon, D.
Copyright Year	2010
Description	Author affiliation: Clayton School of Information Technology, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia (Matharage, S.; Alahakoon, D.)
Abstract	Growing Self Organizing Map (GSOM) has proven benefits in text clustering. Latent Semantic Analysis (LSA) also has been used in text clustering to capture the latent concepts from text. This paper presents a novel combination of GSOM and LSA to improve text clustering results compared to using GSOM on its own. LSA is an inherently global algorithm that looks at trends and patterns globally and GSOM is a nearest neighborhood based algorithm which looks at local patterns. Combination of these two can be used to discover both the global and local patterns. In the proposed model, initial text corpus is converted into its vector space representation using the traditional Term Frequency — Inverse Document Frequency (TF-IDF) technique. Then the Singular Value Decomposition (SVD) followed by Frobenius norm is applied on the resulting high dimensional vector to come up with a new vector with an optimal number of dimensions. Experiments using the proposed model were conducted and compared with the original GSOM under the same conditions. Experiment results demonstrate that the new combination of these well known techniques enhances the accuracy of clustering results and the computational time than the GSOM alone.
Starting Page	441
Ending Page	446
File Size	267366
Page Count	6
File Format	PDF
ISBN	9781424485499
e-ISBN	9781424485529
DOI	10.1109/ICIAFS.2010.5715702
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-17
Publisher Place	Sri Lanka
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Growing Self Organizing Map text clustering Neurons Semantics Clustering algorithms Artificial neural networks Latent Semantic Analysis Matrix decomposition Organizing
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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