M. Shahriar Hossain, Naren Ramakrishnan, Ian Davidson, Layne T. Watson
Given a clustering algorithm, how can we adapt it to find multiple, nonredundant, high-quality clusterings? We focus on algorithms based on vector quantization and describe a framework for automatic ‘alternatization’ of such algorithms. Our framework works in both simultaneous and sequential learning formulations and can mine an arbitrary number of alternative clusterings. We demonstrate its applicability to various clustering algorithms—k-means, spectral clustering, constrained clustering, and co-clustering—and effectiveness in mining a variety of datasets.
- Date of publication:
- September 1, 2013
- IEEE International Conference on Data Mining
- Springer Science + Business Media
- Page number(s):
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