Over the last years, researchers have focused their attention on a new approach, supervised clustering, that combines the main characteristics of both traditional clustering and supervised classification tasks. Motivated by the importance of the initialization in the traditional clustering context, this paper explores to what extent supervised initialization step could help traditional clustering to obtain better performances on supervised clustering tasks. This paper reports experiments which show that the simple proposed approach yields a good solution together with significant reduction of the computational cost.
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