Abstract
In this paper, we introduce an approach to integrate prior knowledge in cluster analysis, which is different from the existing ones for semi-supervised clustering methods. In order to aid the discovery of alternative structures present in the data, we consider the knowledge of some existing complete classification of such data. The approach proposed is based on our Multi-Objective Clustering Ensemble algorithm (MOCLE). This algorithm generates a concise and stable set of partitions, which represents different trade-offs between several measures of partition quality. The prior knowledge is automatically integrated in MOCLE by embedding it into one of the objective functions. In this case, the function gives as output the quality of a partition, considering the prior knowledge of one of the known structures of the data.
This work was supported by FAPESP and CNPq.
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Faceli, K., de Carvalho, A.C.P.L.F., de Souto, M.C.P. (2007). Multi-Objective Clustering Ensemble with Prior Knowledge. In: Sagot, MF., Walter, M.E.M.T. (eds) Advances in Bioinformatics and Computational Biology. BSB 2007. Lecture Notes in Computer Science(), vol 4643. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73731-5_4
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DOI: https://doi.org/10.1007/978-3-540-73731-5_4
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