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

作者：Campello R J G B^*; Moulavi D; Zimek A; Sander J

来源：Data Mining and Knowledge Discovery, 2013, 27(3): 344-371.

DOI：10.1007/s10618-013-0311-4

• Hierarchical clustering
• Optimal selection of clusters
• Should-link and should-not-link constraints
• Cluster quality

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.

• 出版日期2013-11