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 performance. 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 simulated 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 Fβ-measure and Fb3-measure over two gold standard data sets.