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. © 2011 Springer-Verlag.
CITATION STYLE
Xiong, T., Wang, S., Mayers, A., & Monga, E. (2011). Semi-supervised parameter-free divisive hierarchical clustering of categorical data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6634 LNAI, pp. 265–276). Springer Verlag. https://doi.org/10.1007/978-3-642-20841-6_22
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