[hal-01077207] Multi-Objective Search Results Clustering

Most web search results clustering SRC strategies have predominantly studied the definition of adapted representation spaces to the detriment of new clustering techniques to improve perfor-mance In this paper we define SRC as a multi-objective optimization MOO problem to take advantage of most recent works in clustering In particular we define two objective functions compactness and separability which are simultaneously optimized using a MOO-based simu-lated annealing technique called AMOSA The proposed algorithm is able to automatically detect the number of clusters for any query and outperforms all state-of-the-art text-based solutions in terms of F β -measure and F b 3 -measure over two gold standard data sets



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