Abstract
Semi-supervised clustering can yield considerable improvement over unsupervised clustering. Most existing semi-supervised clustering algorithms are non-hierarchical, derived from the k-means algorithm and designed for analyzing numeric data. Clustering categorical data is a challenging issue due to the lack of inherently meaningful similarity measure, and semi-supervised clustering in the categorical domain remains untouched. In this paper, we propose a novel semi-supervised divisive hierarchical algorithm for categorical data. Our algorithm is parameter-free, fully automatic and effective in taking advantage of instance-level constraint background knowledge to improve the quality of the resultant dendrogram. Experiments on real-life data demonstrate the promising performance of our algorithm.
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Xiong, T., Wang, S., Mayers, A., Monga, E. (2011). Semi-supervised Parameter-Free Divisive Hierarchical Clustering of Categorical Data. In: Huang, J.Z., Cao, L., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 6634. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20841-6_22
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DOI: https://doi.org/10.1007/978-3-642-20841-6_22
Publisher Name: Springer, Berlin, Heidelberg
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