A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies
Campello, R.J.G.B., Moulavi, D., Zimek, A., and Sander, J. (2013) A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies. Data Mining and Knowledge Discovery, 27 (3). pp. 344-371.
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Abstract
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.
| Item ID: | 47615 |
|---|---|
| Item Type: | Article (Research - C1) |
| ISSN: | 1573-756X |
| Keywords: | hierarchical clustering, optimal selection of clusters, should-link and should-not-link constraints, cluster quality |
| Funders: | São Paulo Research Foundation (FAPESP), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Natural Sciences and Engineering Research Council of Canada (NSERC) |
| Date Deposited: | 08 Mar 2017 07:40 |
| FoR Codes: | 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100% |
| SEO Codes: | 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100% |
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