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A fragment-based iterative consensus clustering algorithm with a robust similarity

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Abstract

The consensus clustering technique combines multiple clustering results without accessing the original data. Consensus clustering can be used to improve the robustness of clustering results or to obtain the clustering results from multiple data sources. In this paper, we propose a novel definition of the similarity between points and clusters. With an iterative process, such a definition of similarity can represent how a point should join or leave a cluster clearly, determine the number of clusters automatically, and combine partially overlapping clustering results. We also incorporate the concept of “clustering fragment” into our method for increased speed. The experimental results show that our algorithm achieves good performances on both artificial data and real data.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Chih-Heng Chung & Bi-Ru Dai

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  1. Chih-Heng Chung
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  2. Bi-Ru Dai
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Correspondence to Bi-Ru Dai.

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Chung, CH., Dai, BR. A fragment-based iterative consensus clustering algorithm with a robust similarity. Knowl Inf Syst 41, 591–609 (2014). https://doi.org/10.1007/s10115-013-0667-1

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  • DOI: https://doi.org/10.1007/s10115-013-0667-1

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