Abstract
Semi-supervised clustering ensemble has emerged as an important elaboration of classical clustering problem that improves quality and robustness in clustering by combining the results of different clustering components with user provided constraints. In this paper, we propose a novel semi-supervised consensus clustering algorithm based on multi-ant colonies. Our method incorporates pairwise constraints not only in each ant colony clustering process, but also in computing new similarity matrix during the multi-ant colonies ensemble. Experimental results demonstrate the effectiveness of the proposed method.
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