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 Feijiang Li

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Average citations per article3.50
Citation Count7
Publication count2
Publication years2014-2017
Available for download0
Average downloads per article0.00
Downloads (cumulative)53
Downloads (12 Months)53
Downloads (6 Weeks)27
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June 2018 ACM Transactions on Knowledge Discovery from Data (TKDD): Volume 12 Issue 5, July 2018
Publisher: ACM
Citation Count: 0
Downloads (6 Weeks): 27,   Downloads (12 Months): 53,   Downloads (Overall): 53

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Clustering ensemble has drawn much attention in recent years due to its ability to generate a high quality and robust partition result. Weighted clustering ensemble and selective clustering ensemble are two general ways to further improve the performance of a clustering ensemble method. Existing weighted clustering ensemble methods assign the ...
Keywords: Clustering ensemble, cluster quality, selective clustering ensemble, weighted clustering ensemble

February 2017 Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal: Volume 378 Issue C, February 2017
Publisher: Elsevier Science Inc.
Citation Count: 2

Clustering analysis is a fundamental technique in machine learning, which is also widely used in information granulation. Multiple clustering systems granulate a data set into multiple granular structures. Therefore, clustering ensemble can serve as an important branch of multigranulation information fusion. Many approaches have been proposed to solve the clustering ...
Keywords: Clustering ensemble, Dempster-Shafer evidence theory, Information fusion, Multigranulation

March 2014 International Journal of Approximate Reasoning: Volume 55 Issue 3, March, 2014
Publisher: Elsevier Science Inc.
Citation Count: 3

Set-based granular computing plays an important role in human reasoning and problem solving. Its three key issues constitute information granulation, information granularity and granular operation. To address these issues, several methods have been developed in the literature, but no unified framework has been formulated for them, which could be inefficient ...
Keywords: Knowledge distance, Rough set theory, Information granularity, Operator, Granular computing

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