A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies

• Autores: R. Campello, D. Moulavi, Arthur Zimek, J. Sander
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 27, Nº 3, 2013, págs. 344-371
• Idioma: inglés
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• Resumen

  • 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.