Abstract
Recently, several algorithms have been introduced for enhancing clustering quality by using supervision in the form of constraints. These algorithms typically utilize the pair wise constraints to either modify the clustering objective function or to learn the clustering distance measure. Very few of these algorithms show the ability of discovering clusters of different shapes along with satisfying the provided constraints. In this paper, a novel semi-supervised clustering algorithm is introduced that uses the side information and finds clusters of arbitrary shapes. This algorithm uses a two-stage clustering approach satisfying the pair wise constraints. In the first stage, the data points are grouped into a relatively large number of fuzzy ellipsoidal sub-clusters. Then, in the second stage, connections between sub-clusters are established according to the pair wise constraints and the similarity of sub-clusters. Experimental results show the ability of the proposed algorithm for finding clusters of arbitrary shapes.
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Soleymani Baghshah, M., Bagheri Shouraki, S. (2008). A Novel Semi-supervised Clustering Algorithm for Finding Clusters of Arbitrary Shapes. In: Sarbazi-Azad, H., Parhami, B., Miremadi, SG., Hessabi, S. (eds) Advances in Computer Science and Engineering. CSICC 2008. Communications in Computer and Information Science, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89985-3_123
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DOI: https://doi.org/10.1007/978-3-540-89985-3_123
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