Using background knowledge in clustering, called semi-clustering, is one of the actively researched areas in data mining. In this paper, we illustrate how to use background knowledge related to a domain more efficiently. For a given data, the number of classes is investigated by using the must-link constraints before clustering and these must-link data are assigned to the corresponding classes. When the clustering algorithm is applied, we make use of the cannot-link constraints for assignment. The proposed clustering approach improves the result of COP k-means by about 10%. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Shin, K., & Abraham, A. (2006). Two phase semi-supervised clustering using background knowledge. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4224 LNCS, pp. 707–712). Springer Verlag. https://doi.org/10.1007/11875581_85
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