LWMC: A Locally Weighted Meta-Clustering Algorithm for Ensemble Clustering
The last decade has witnessed a rapid development of the ensemble clustering technique. Despite the great progress that has been made, there are still some challenging problems in the ensemble clustering research. In this paper, we aim to address two of the challenging problems in ensemble clustering, that is, the local weighting problem and the scalability problem. Specifically, a locally weighted meta-clustering (LWMC) algorithm is proposed, which is featured by two main advantages. First, it is highly efficient, due to its ability of working and voting on clusters. Second, it incorporates a locally weighted voting strategy in the meta-clustering process, which can exploit the diversity of clusters by means of local uncertainty estimation and ensemble-driven cluster validity. Experiments on eight real-world datasets demonstrate the superiority of the proposed algorithm in both clustering quality and efficiency.
KeywordsEnsemble clustering Consensus clustering Meta-clustering Local weighting Scalability
This work was supported by NSFC (61602189, 61502543 and 61573387), the Ph.D. Start-up Fund of Natural Science Foundation of Guangdong Province, China (2016A030310457), Guangdong Natural Science Funds for Distinguished Young Scholars (2016A030306014), and the Tip-Top Scientific and Technical Innovative Youth Talents of Guangdong Special Support Program (No. 2016TQ03X542).
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