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» From Comparing Clusterings to Combining Clusterings
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CEC
2009
IEEE
14 years 1 months ago
Automatic clustering with multi-objective Differential Evolution algorithms
—This paper applies the Differential Evolution (DE) algorithm to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework. It compares the performa...
Kaushik Suresh, Debarati Kundu, Sayan Ghosh, Swaga...
KDD
2004
ACM
132views Data Mining» more  KDD 2004»
14 years 9 months ago
A probabilistic framework for semi-supervised clustering
Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clu...
Sugato Basu, Mikhail Bilenko, Raymond J. Mooney
MLMTA
2007
13 years 10 months ago
Consensus Based Ensembles of Soft Clusterings
— Cluster Ensembles is a framework for combining multiple partitionings obtained from separate clustering runs into a final consensus clustering. This framework has attracted mu...
Kunal Punera, Joydeep Ghosh
NIPS
2004
13 years 10 months ago
Limits of Spectral Clustering
An important aspect of clustering algorithms is whether the partitions constructed on finite samples converge to a useful clustering of the whole data space as the sample size inc...
Ulrike von Luxburg, Olivier Bousquet, Mikhail Belk...
COLING
2010
13 years 4 months ago
Comparing Language Similarity across Genetic and Typologically-Based Groupings
Recent studies have shown the potential benefits of leveraging resources for resource-rich languages to build tools for similar, but resource-poor languages. We examine what const...
Ryan Georgi, Fei Xia, William Lewis