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A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies. (English) Zbl 1281.68175
Summary: We introduce a framework for the optimal extraction of flat clusterings from local cuts through cluster hierarchies. The extraction of a flat clustering from a cluster tree is formulated as an optimization problem and a linear complexity algorithm is presented that provides the globally optimal solution to this problem in semi-supervised as well as in unsupervised scenarios. A collection of experiments is presented involving clustering hierarchies of different natures, a variety of real data sets, and comparisons with specialized methods from the literature.

MSC:
68T05 Learning and adaptive systems in artificial intelligence
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