Abstract
Consensus clustering has emerged as an important extension of the classical clustering problem. Given a set of input clusterings of a given dataset, consensus clustering aims to find a single final clustering which is a better fit in some sense than the existing clusterings. There is a significant drawback in generating a single consensus clustering since different input clusterings could differ significantly. In this paper, we develop a new framework, called Multiple Consensus Clustering (MCC), to explore multiple clustering views of a given dataset from a set of input clusterings. Instead of generating a single consensus, we propose two sets of approaches to obtain multiple consensus. One employs the meta clustering method, and the other uses a hierarchical tree structure and further applies a dynamic programming algorithm to generate a flat partition from the hierarchical tree using the modularity measure. Multiple consensuses are finally obtained by applying consensus clustering algorithms to each cluster of the partition. Extensive experimental results on 11 real-world datasets and a case study on a Protein-Protein Interaction (PPI) dataset demonstrate the effectiveness of the MCC framework.
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Notes
The software can be downloaded from http://glaros.dtc.umn.edu/gkhome/views/metis/.
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Funding
The work is partially supported by NSF grants DBI-0850203 and HRD-0833093. This work is partially supported by the National Science Foundation under grants DBI-0850203, HRD-0833093, CNS-1126619, IIS-1213026, and CNS-1461926, the U.S. Department of Homeland Security VACCINE Center under Award Number 2009-ST-061-CI0001, and an FIU Dissertation Year Fellowship.
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Li, T., Zhang, Y., Wang, D. et al. MCC: a Multiple Consensus Clustering Framework. J Classif 36, 414–434 (2019). https://doi.org/10.1007/s00357-019-09318-4
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DOI: https://doi.org/10.1007/s00357-019-09318-4