Abstract
Constrained clustering uses pairwise constraints, i.e., pairs of data that belong to the same or different clusters, to indicate the user-desired contents. In this paper, we propose a new constrained clustering algorithm, which can utilize both must-link and cannot-link constraints. It first adaptively determines the influence range of each constrained data, and then performs clustering on the expanded range of data. The promising experiments on the real-world data sets demonstrate the effectiveness of our method.
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