Active semi-supervision for pairwise constrained clustering

Sugato Basu, Arindam Banerjee, Raymond J. Mooney

Research output: Contribution to conferencePaperpeer-review


Semi-supervised clustering uses a small amount of supervised data to aid unsupervised learning. One typical approach specifies a limited number of must-link and cannot-link constraints between pairs of examples. This paper presents a pairwise constrained clustering framework and a new method for actively selecting informative pairwise constraints to get improved clustering performance. The clustering and active learning methods are both easily scalable to large datasets, and can handle very high dimensional data. Experimental and theoretical results confirm that this active querying of pairwise constraints significantly improves the accuracy of clustering when given a relatively small amount of supervision.

Original languageEnglish (US)
Number of pages12
StatePublished - 2004
Externally publishedYes
EventProceedings of the Fourth SIAM International Conference on Data Mining - Lake Buena Vista, FL, United States
Duration: Apr 22 2004Apr 24 2004


ConferenceProceedings of the Fourth SIAM International Conference on Data Mining
Country/TerritoryUnited States
CityLake Buena Vista, FL

ASJC Scopus subject areas

  • Mathematics(all)


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