TY - CONF JO - IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint TI - Clustering of web search results based on an Iterative Fuzzy C-means Algorithm and Bayesian Information Criterion T2 - IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint IS - SN - VO - SP - 507 EP - 512 AU - Cobos, Carlos AU - Mendoza, Martha AU - Manic, Milos AU - Leon, Elizabeth AU - Herrera-Viedma, Enrique Y1 - 24-28 June 2013 PY - 2013 KW - Algorithm design and analysis KW - Partitioning algorithms KW - Web search KW - Bayesian information criterion KW - Fuzzy c-means KW - Web document clustering VL - SN - 9781479903474 JA - 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS) DO - 10.1109/IFSA-NAFIPS.2013.6608452 AB - 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, SSLk and other metrics. Results show a considerable improvement in clustering quality and performance. ER -