The objective of giving the constraint to semi-supervised clustering, is the increasing of clustering result accuracy. Sometime, it does not give any effect, even can decrease the clustering result accuracy. So, to solve that, before clustering begin, we must filter the constraint. We will filter the constraint by constraint utility, informativeness and coherence. The experiment result shows, that the increasing of clustering result accuracy with giving the high infomativeness and coherence, can not valid for any kind of data. In Iris, Ionosphere, and Digit data, it will give better results, but in Wine, Protein, and Letter give the opposite results.
Keywords: Semi-supervised clustering, constraint, informativeness, coherence
.Basu, Sugato, http://www.cs.utexas.edu/users/ml/risc, diakses pada bulan November 2007.
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