Robust, complete, and efficient correlation clustering

Elke Achtert*, Christian Böhm, Hans Peter Kriegel, Peer Kröger, Arthur Zimek

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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-theart correlation clustering methods in terms of runtime, accuracy, and completeness of the results.

Original languageEnglish
Title of host publicationProceedings of the 7th SIAM International Conference on Data Mining
EditorsChid Apte, David Skillicorn, Bing Liu, Srinivasan Parthasarathy
PublisherSociety for Industrial and Applied Mathematics
Publication dateDec 2007
Pages413-418
ISBN (Print)978-0-89871-630-6
ISBN (Electronic)978-1-61197-277-1
DOIs
Publication statusPublished - Dec 2007
Externally publishedYes
Event7th SIAM International Conference on Data Mining - Minneapolis, MN, United States
Duration: 26. Apr 200728. Apr 2007

Conference

Conference7th SIAM International Conference on Data Mining
CountryUnited States
CityMinneapolis, MN
Period26/04/200728/04/2007

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Clustering algorithms
Scalability

Cite this

Achtert, E., Böhm, C., Kriegel, H. P., Kröger, P., & Zimek, A. (2007). Robust, complete, and efficient correlation clustering. In C. Apte, D. Skillicorn, B. Liu, & S. Parthasarathy (Eds.), Proceedings of the 7th SIAM International Conference on Data Mining (pp. 413-418). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611972771.37
Achtert, Elke ; Böhm, Christian ; Kriegel, Hans Peter ; Kröger, Peer ; Zimek, Arthur. / Robust, complete, and efficient correlation clustering. Proceedings of the 7th SIAM International Conference on Data Mining. editor / Chid Apte ; David Skillicorn ; Bing Liu ; Srinivasan Parthasarathy. Society for Industrial and Applied Mathematics, 2007. pp. 413-418
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Achtert, E, Böhm, C, Kriegel, HP, Kröger, P & Zimek, A 2007, Robust, complete, and efficient correlation clustering. in C Apte, D Skillicorn, B Liu & S Parthasarathy (eds), Proceedings of the 7th SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, pp. 413-418, 7th SIAM International Conference on Data Mining, Minneapolis, MN, United States, 26/04/2007. https://doi.org/10.1137/1.9781611972771.37

Robust, complete, and efficient correlation clustering. / Achtert, Elke; Böhm, Christian; Kriegel, Hans Peter; Kröger, Peer; Zimek, Arthur.

Proceedings of the 7th SIAM International Conference on Data Mining. ed. / Chid Apte; David Skillicorn; Bing Liu; Srinivasan Parthasarathy. Society for Industrial and Applied Mathematics, 2007. p. 413-418.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Achtert E, Böhm C, Kriegel HP, Kröger P, Zimek A. Robust, complete, and efficient correlation clustering. In Apte C, Skillicorn D, Liu B, Parthasarathy S, editors, Proceedings of the 7th SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics. 2007. p. 413-418 https://doi.org/10.1137/1.9781611972771.37