Abstract
Semi-supervised approaches have proven to be efficient in clustering tasks. They allow user input, thus enhancing the quality of the clustering. However, the user intervention is generally limited to integrate boolean constraints in form of must-link and cannot-link constraints between pairs of objects. This paper investigates the issue of satisfying ranked constraints in performing hierarchical clustering. \(\mathcal{SHACUN}\) is a new introduced method for handling cases when some constraints are more important than others and must be firstly enforced. Carried out experiments on real log files used for decision-maker groupization in data warehouse confirm the soundness of our approach.
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Ben Ahmed, E., Nabli, A., Gargouri, F. (2012). \(\mathcal{SHACUN}\): Semi-supervised Hierarchical Active Clustering Based on Ranking Constraints. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2012. Lecture Notes in Computer Science(), vol 7377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31488-9_16
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DOI: https://doi.org/10.1007/978-3-642-31488-9_16
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