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A hyper-heuristic approach to design and tuning heuristic methods for web document clustering

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3 Author(s)
Cobos, C. ; Comput. Sci. Dept., Univ. del Cauca, Popayan, Colombia ; Mendoza, M. ; León, E.

This paper introduces a new description-centric algorithm for web document clustering called HHWDC. The HHWDC algorithm has been designed from a hyper-heuristic approach and allows defining the best algorithm for web document clustering. HHWDC uses as heuristic selection methodology two options, namely: random selection and roulette wheel selection based on performance of low-level heuristics (harmony search, an improved harmony search, a novel global harmony search, global-best harmony search, restrictive mating, roulette wheel selection, and particle swarm optimization). HHWDC uses the k-means algorithm for local solution improvement strategy, and based on the Bayesian Information Criteria is able to automatically define the number of clusters. HHWDC uses two acceptance/replace strategies, namely: Replace the worst and Restricted Competition Replacement. HHWDC was tested with data sets based on Reuters-21578 and DMOZ, obtaining promising results (better precision results than a Singular Value Decomposition algorithm).

Published in:

Evolutionary Computation (CEC), 2011 IEEE Congress on

Date of Conference:

5-8 June 2011

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