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Article Dans Une Revue IEEE Transactions on Cybernetics Année : 2020

Transfer Clustering Ensemble Selection

Résumé

Clustering ensemble (CE) takes multiple clusteringsolutions into consideration in order to effectively improve theaccuracy and robustness of the final result. To reduce redundancyas well as noise, a CE selection (CES) step is added to furtherenhance performance. Quality and diversity are two importantmetrics of CES. However, most of the CES strategies adoptheuristic selection methods or a threshold parameter setting toachieve tradeoff between quality and diversity. In this paper, wepropose a transfer CES (TCES) algorithm which makes use of therelationship between quality and diversity in a source dataset, andtransfers it into a target dataset based on three objective functions.Furthermore, a multiobjective self-evolutionary process isdesigned to optimize these three objective functions. Finally, weconstruct a transfer CE framework (TCE-TCES) based on TCESto obtain better clustering results. The experimental results on 12transfer clustering tasks obtained from the 20newsgroups datasetshow that TCE-TCES can find a better tradeoff between qualityand diversity, as well as obtaining more desirable clusteringresults.
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Dates et versions

hal-01980313 , version 1 (14-01-2019)

Identifiants

Citer

Yifan Shi, C.L. Philip Chen, Zhiwen Yu, Jane You, Hau-San Wong, et al.. Transfer Clustering Ensemble Selection. IEEE Transactions on Cybernetics, 2020, 50 (6), pp.2872-2885. ⟨10.1109/TCYB.2018.2885585⟩. ⟨hal-01980313⟩
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