Carlos Cobos1, Martha Mendoza1, Elizabeth León2, Milos Manic3, and Enrique Herrera-Viedma4
1 University of Cauca, Colombia (e-mail: ccobos@unicauca.edu.co, mmendoza@unicauca.edu.co).
2 Universidad Nacional de Colombia, Colombia (e-mail: eleonguz@unal.edu.co).
3 University of Idaho at Idaho Falls, USA (e-mail: misko@uidaho.edu)
4 University of Granada, Spain (e-mail: viedma@decsai.ugr.es)
]]> Manuscript received on March 13, 2013.
Abstract
As resources become more and more available on the Web, so the difficulties associated with finding the desired information increase. Intelligent agents can assist users in this task since they can search, filter and organize information on behalf of their users. Web document clustering techniques can also help users to find pages that meet their information requirements. This paper presents a personalized web document clustering called TopicSearch. TopicSearch introduces a novel inverse document frequency function to improve the query expansion process, a new memetic algorithm for web document clustering, and frequent phrases approach for defining cluster labels. Each user query is handled by an agent who coordinates several tasks including query expansion, search results acquisition, preprocessing of search results, cluster construction and labeling, and visualization. These tasks are performed by specialized agents whose execution can be parallelized in certain instances. The model was successfully tested on fifty DMOZ datasets. The results demonstrated improved precision and recall over traditional algorithms (k-means, Bisecting k-means, STC y Lingo). In addition, the presented model was evaluated by a group of twenty users with 90% being in favor of the model.
Key words: Web document clustering, intelligent agents, query expansion, WordNet, memetic algorithms, user profile.
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ACKNOWLEDGMENT
]]> The work in this paper was supported by a Research Grant from the University of Cauca under Project VRI-2560 and the National University of Colombia.
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