21st International Conference on Data Engineering (ICDE'05) (2005)

Tokyo, Japan

Apr. 5, 2005 to Apr. 8, 2005

ISSN: 1084-4627

ISBN: 0-7695-2285-8

pp: 341-352

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2005.34

Aristides Gionis , University of Helsinki

Heikki Mannila , University of Helsinki

Panayiotis Tsaparas , University of Helsinki

ABSTRACT

We consider the following problem: given a set of clusterings, find a clustering that agrees as much as possible with the given clusterings. This problem, clustering aggregation, appears naturally in various contexts. For example, clustering categorical data is an instance of the problem: each categorical variable can be viewed as a clustering of the input rows. Moreover, clustering aggregation can be used as a meta-clustering method to improve the robustness of clusterings. The problem formulation does not require a-priori information about the number of clusters, and it gives a naturalway for handlingmissing values. We give a formal statement of the clustering-aggregation problem, we discuss related work, and we suggest a number of algorithms. For several of the methods we provide theoretical guarantees on the quality of the solutions. We also show how sampling can be used to scale the algorithms for large data sets. We give an extensive empirical evaluation demonstrating the usefulness of the problem and of the solutions.

INDEX TERMS

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CITATION

P. Tsaparas, H. Mannila and A. Gionis, "Clustering Aggregation,"

*21st International Conference on Data Engineering (ICDE'05)(ICDE)*, Tokyo, Japan, 2005, pp. 341-352.

doi:10.1109/ICDE.2005.34

CITATIONS