基于凸差规划的不完整数据填充聚类

doi:10.16451/j.cnki.issn1003-6059.201701009

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Abstract To improve the clustering performance, an incomplete data imputation clustering algorithm based on difference of convex functions programming (DCP) is proposed. DCP is applied to optimize the kernel-based fuzzy C-means objective function, and the alternative optimization process for DCP clustering and missing completion is given. The convergence of the alternating optimization is proved theoretically. Experiments show the superiority of the proposed algorithm in missing completion and clustering performance.

Key words： ncomplete Data Missing Completion Difference of Convex Functions Programming(DCP) Kernel Based Fuzzy C means Clustering

Received: 10 September 2016

ZTFLH:

TP 391.4

About author:: HE Dan, born in 1992, master student. Her research interests include pattern recognition. CHEN Songcan(Corresponding author), born in 1962, Ph.D., professor. His research interests include pattern recognition, machine learning and intelligent computing.

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Cite this article:

HE Dan,CHEN Songcan. Incomplete Data Imputation Clustering Based on Difference of Convex Functions Programming[J]. , 2017, 30(1): 81-88.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201701009 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I1/81

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