M. Shahriar Hossain, Naren Ramakrishnan, Ian Davidson, Layne T. Watson

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.

People

ltw-updated

Layne T. Watson


Ramakrishnan-updated

Naren Ramakrishnan


Publication Details

Date of publication:
September 1, 2013
Conference:
IEEE International Conference on Data Mining
Publisher:
Springer Science + Business Media
Page number(s):
193--224
Volume:
27
Issue Number:
2