Transfer Clustering Ensemble Selection

doi:10.1109/TCYB.2018.2885585

Transfer Clustering Ensemble Selection

2020-06-01

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AI

陈晨 (C. L. Philip Chen)

DOI：10.1109/TCYB.2018.2885585

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Clustering ensemble (CE) takes multiple clustering solutions into consideration in order to effectively improve the accuracy and robustness of the final result. To reduce redundancy as well as noise, a CE selection (CES) step is added to further enhance performance. Quality and diversity are two important metrics of CES. However, most of the CES strategies adopt heuristic selection methods or a threshold parameter setting to achieve tradeoff between quality and diversity. In this paper, we propose a transfer CES (TCES) algorithm which makes use of the relationship between quality and diversity in a source dataset, and transfers it into a target dataset based on three objective functions. Furthermore, a multiobjective self-evolutionary process is designed to optimize these three objective functions. Finally, we construct a transfer CE framework (TCE-TCES) based on TCES to obtain better clustering results. The experimental results on 12 transfer clustering tasks obtained from the 20newsgroups dataset show that TCE-TCES can find a better tradeoff between quality and diversity, as well as obtaining more desirable clustering results.

Keywords:

Clustering algorithms

Partitioning algorithms

Task analysis

Micromechanical devices

Redundancy

Indexes

Cybernetics

Clustering ensemble selection (CES)

machine learning

multiobjective

transfer learning

Transfer Clustering Ensemble Selection

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