Abstract
Semi-supervised clustering aims at incorporating the known prior knowledge into the clustering process to achieve better performance. Recently, semi-supervised clustering with pairwise constraints has emerged as an important variant of the traditional clustering paradigm. In this paper, the disadvantages of pairwise constraints are analyzed in detail. To address these disadvantages, exemplars-constraints are firstly illustrated. Then based on the exemplars-constraints, a semi-supervised clustering framework is described step by step, and an exemplars-constraints EM algorithm is designed. Finally several UCI datasets are selected for experiments, and the experimental results show that exemplars-constraints can work well and the proposed algorithm can outperform the corresponding unsupervised clustering algorithm and the semi-supervised algorithms based on pairwise constraints.
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Wang, H., Li, T., Li, T., Yang, Y. (2012). Exemplars-Constraints for Semi-supervised Clustering. In: Zhou, S., Zhang, S., Karypis, G. (eds) Advanced Data Mining and Applications. ADMA 2012. Lecture Notes in Computer Science(), vol 7713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35527-1_10
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DOI: https://doi.org/10.1007/978-3-642-35527-1_10
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