• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

A new Ensemble Clustering Algorithm using a Reconstructed Mapping Coefficient


Abstract

Ensemble clustering commonly integrates multiple basic partitions to obtain a more accurate clustering result than a single partition. Specifically, it exists an inevitable problem that the incomplete transformation from the original space to the integrated space. In this paper, a novel ensemble clustering algorithm using a newly reconstructed mapping coefficient (ECRMC) is proposed. In the algorithm, a newly reconstructed mapping coefficient between objects and micro-clusters is designed based on the principle of increasing information entropy to enhance effective information. This can reduce the information loss in the transformation from micro-clusters to the original space. Then the correlation of the micro-clusters is creatively calculated by the Spearman coefficient. Therefore, the revised co-association graph between objects can be built more accurately because the supplementary information can well ensure the completeness of the whole conversion process. Experiment results demonstrate that the ECRMC clustering algorithm has high performance, effectiveness, and feasibility.


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Cite this article

[IEEE Style]
T. Cao, D. Chang and Y. Zhao, "A new Ensemble Clustering Algorithm using a Reconstructed Mapping Coefficient," KSII Transactions on Internet and Information Systems, vol. 14, no. 7, pp. 2957-2980, 2020. DOI: 10.3837/tiis.2020.07.013.

[ACM Style]
Tuoqia Cao, Dongxia Chang, and Yao Zhao. 2020. A new Ensemble Clustering Algorithm using a Reconstructed Mapping Coefficient. KSII Transactions on Internet and Information Systems, 14, 7, (2020), 2957-2980. DOI: 10.3837/tiis.2020.07.013.