Link-Based Cluster Ensemble Method for Improved Meta-clustering Algorithm

  • Changlong Shao
  • Shifei DingEmail author
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 581)


Ensemble clustering has become a hot research field in intelligent information processing and machine learning. Although significant progress has been made in recent years, there are still two challenging issues in the current ensemble clustering research. First of all, most ensemble clustering algorithms tend to explore similarity at the level of object but lack the ability to explore information at the level of cluster. Secondly, many ensemble clustering algorithms only focus on the direct relationship, while ignoring the indirect relationship between clusters. In order to solve these two problems, a link-based meta-clustering algorithm (L-MCLA) have been proposed in this paper. A series of experiment results demonstrate that the proposed algorithm not only produces better clustering effect but is also less influenced by different ensemble sizes.


Inter-cluster similarity Ensemble clustering Clustering Connected triple Meta-clustering algorithm (MCLA) 



This work is supported by the National Natural Science Foundation of China under Grant No. 61672522 and No. 61976216.


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Copyright information

© IFIP International Federation for Information Processing 2020

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyChina University of Mining and TechnologyXuzhouChina
  2. 2.Mine Digitization Engineering Research Center of Ministry of Education of the People’s Republic of ChinaXuzhouChina

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