Data Mining and Knowledge Discovery

, Volume 27, Issue 2, pp 193–224

How to “alternatize” a clustering algorithm

  • M. Shahriar Hossain
  • Naren Ramakrishnan
  • Ian Davidson
  • Layne T. Watson
Article

DOI: 10.1007/s10618-012-0288-4

Cite this article as:
Hossain, M.S., Ramakrishnan, N., Davidson, I. et al. Data Min Knowl Disc (2013) 27: 193. doi:10.1007/s10618-012-0288-4

Abstract

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.

Keywords

ClusteringAlternative clustering

Copyright information

© The Author(s) 2012

Authors and Affiliations

  • M. Shahriar Hossain
    • 1
  • Naren Ramakrishnan
    • 2
  • Ian Davidson
    • 4
  • Layne T. Watson
    • 2
    • 3
  1. 1.Department of Mathematics and Computer ScienceVirginia State UniversityPetersburgUSA
  2. 2.Department of Computer ScienceVirginia Polytechnic Institute and State UniversityBlacksburgUSA
  3. 3.Department of MathematicsVirginia Polytechnic Institute and State UniversityBlacksburgUSA
  4. 4.Department of Computer ScienceUniversity of CaliforniaDavisUSA