NDLI: Clustering of web search results based on an Iterative Fuzzy C-means Algorithm and Bayesian Information Criterion

Content Provider	IEEE Xplore Digital Library
Author	Cobos, C. Mendoza, M. Leon, E. Manic, M. Herrera-Viedma, E.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci. & Artificial Intell., Univ. of Granada, Granada, Spain (Herrera-Viedma, E.) \|\| Comput. Sci. Dept., Univ. del Cauca, Popayan, Colombia (Cobos, C.; Mendoza, M.) \|\| Syst. & Ind. Dept., Univ. Nac. de Colombia, Bogota, Colombia (Leon, E.) \|\| Dept. of Comput. Sci., Univ. of Idaho at Idaho Falls, Idaho Falls, ID, USA (Manic, M.)
Abstract	The clustering of web search has become a very interesting research area among academic and scientific communities involved in information retrieval. Clustering of web search result systems, also called Web Clustering Engines, seek to increase the coverage of documents presented for the user to review, while reducing the time spent reviewing them. Several algorithms for web document clustering already exist, but results show there is room for more to be done. This paper introduces a new description-centric algorithm for clustering of web results called IFCWR. IFCWR initially selects a maximum estimated number of clusters using Forgy's strategy, then it iteratively merges clusters until results cannot be improved. Every merge operation implies the execution of Fuzzy C-Means for clustering results of web search and the calculus of Bayesian Information Criterion for automatically evaluating the best solution and number of clusters. IFCWR was compared against other established web document clustering algorithms, among them: Suffix Tree Clustering and Lingo. Comparison was executed on AMBIENT and MORESQUE datasets, using precision, recall, f-measure, $SSL_{k}$ and other metrics. Results show a considerable improvement in clustering quality and performance.
Sponsorship	IEEE
Starting Page	507
Ending Page	512
File Size	345282
Page Count	6
File Format	PDF
ISBN	9781479903481
DOI	10.1109/IFSA-NAFIPS.2013.6608452
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Partitioning algorithms Algorithm design and analysis Web search Bayes methods Accuracy Educational institutions bayesian information criterion web document clustering fuzzy c-means
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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