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Proceedings of the 2007 SIAM International Conference on Data Mining

Robust, Complete, and Efficient Correlation Clustering

Abstract

Correlation clustering aims at the detection of data points that appear as hyperplanes in the data space and, thus, exhibit common correlations between different subsets of features. Recently proposed methods for correlation clustering usually suffer from several severe drawbacks including poor robustness against noise or parameter settings, incomplete results (i.e. missed clusters), poor usability due to complex input parameters, and poor scalability. In this paper, we propose the novel correlation clustering algorithm COPAC (COrrelation PArtition Clustering) that aims at improved robustness, completeness, usability, and efficiency. Our experimental evaluation empirically shows that COPAC is superior over existing state-of-the-art correlation clustering methods in terms of runtime, accuracy, and completeness of the results.

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cover image Proceedings
Proceedings of the 2007 SIAM International Conference on Data Mining
Pages: 413 - 418
Editors: Chid Apte, IBM T.J. Watson Research Center, Yorktown Heights, New York, David Skillicorn, Queen's University, Kingston, Ontario, Canada, Bing Liu, University of Illinois at Chicago, Chicago, Illinois, and Srinivasan Parthasarathy, The Ohio State University, Columbus, Ohio
ISBN (Print): 978-0-898716-30-6
ISBN (Online): 978-1-61197-277-1

History

Published online: 18 December 2013

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*
Partly supported by the German Ministry for Education, Science, Research and Technology (BMBF) under grant no. 031U212F within the BFAM project.

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