A Split-merge framework for comparing clusterings

Qiaoliang Xiang, Qi Mao, Kian Ming A. Chai, Hai Leong Chieu, Ivor Wai-Hung Tsang, Zhenddong Zhao

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

6 Citations (Scopus)

Abstract

Clustering evaluation measures are frequently used to evaluate the performance of algorithms. However, most measures are not properly normalized and ignore some information in the inherent structure of clusterings. We model the relation between two clusterings as a bipartite graph and propose a general component-based decomposition formula based on the components of the graph. Most existing measures are examples of this formula. In order to satisfy consistency in the component, we further propose a split-merge framework for comparing clusterings of different data sets. Our framework gives measures that are conditionally normalized, and it can make use of data point information, such as feature vectors and pairwise distances. We use an entropy-based instance of the framework and a coreference resolution data set to demonstrate empirically the utility of our framework over other measures.
Original languageEnglish
Title of host publicationProceedings of the 29th International Conference on Machine Learning
Subtitle of host publicationICML 2012 : 26 June-1 July 2012, Edinburgh, Scotland
Place of PublicationGermany
PublisherInternational Machine Learning Society
Pages1055-1062
Number of pages8
Volume2
ISBN (Print)9781450312851
Publication statusPublished - 2012
EventInternational Conference on Machine Learning (29th : 2012) - Edinburgh, Scotland
Duration: 26 Jun 20121 Jul 2012

Conference

ConferenceInternational Conference on Machine Learning (29th : 2012)
CityEdinburgh, Scotland
Period26/06/121/07/12

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  • Cite this

    Xiang, Q., Mao, Q., Chai, K. M. A., Chieu, H. L., Tsang, I. W-H., & Zhao, Z. (2012). A Split-merge framework for comparing clusterings. In Proceedings of the 29th International Conference on Machine Learning: ICML 2012 : 26 June-1 July 2012, Edinburgh, Scotland (Vol. 2, pp. 1055-1062). Germany: International Machine Learning Society.