Adaptive Ensembling of Semi-Supervised Clustering Solutions

Zhiwen Yu, Zongqiang Kuang, Jiming LIU, Hongsheng Chen, Jun Zhang, Jane You, Hau San Wong, Guoqiang Han

Research output: Contribution to journalJournal articlepeer-review

53 Citations (Scopus)


Conventional semi-supervised clustering approaches have several shortcomings, such as (1) not fully utilizing all useful must-link and cannot-link constraints, (2) not considering how to deal with high dimensional data with noise, and (3) not fully addressing the need to use an adaptive process to further improve the performance of the algorithm. In this paper, we first propose the transitive closure based constraint propagation approach, which makes use of the transitive closure operator and the affinity propagation to address the first limitation. Then, the random subspace based semi-supervised clustering ensemble framework with a set of proposed confidence factors is designed to address the second limitation and provide more stable, robust, and accurate results. Next, the adaptive semi-supervised clustering ensemble framework is proposed to address the third limitation, which adopts a newly designed adaptive process to search for the optimal subspace set. Finally, we adopt a set of nonparametric tests to compare different semi-supervised clustering ensemble approaches over multiple datasets. The experimental results on 20 real high dimensional cancer datasets with noisy genes and 10 datasets from UCI datasets and KEEL datasets show that (1) The proposed approaches work well on most of the real-world datasets. (2) It outperforms other state-of-the-art approaches on 12 out of 20 cancer datasets, and 8 out of 10 UCI machine learning datasets.

Original languageEnglish
Article number7904723
Pages (from-to)1577-1590
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number8
Publication statusPublished - Aug 2017

Scopus Subject Areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

User-Defined Keywords

  • clustering
  • Clustering ensemble
  • semi-supervised clustering


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