ON SOME INCREMENTAL ALGORITHMS FOR THE MINIMUM SUM-OF-SQUARES CLUSTERING PROBLEM. PART 2: INCREMENTAL DC ALGORITHMS

被引：0

作者：

Tran Hung Cuong ^{[1
]}

Yao, Jen-Chih ^{[2
]}

Nguyen Dong Yen ^{[3
]}

机构：

[1] Hanoi Univ Ind, Fac Informat Technol, Dept Comp Sci, 298 Cau Dien Rd, Hanoi, Vietnam

[2] China Med Univ, China Med Univ Hosp, Res Ctr Interneural Comp, Taichung, Taiwan

[3] Vietnam Acad Sci & Technol, Inst Math, 18 Hoang Quoc Viet, Hanoi 10307, Vietnam

来源：

JOURNAL OF NONLINEAR AND CONVEX ANALYSIS | 2020年 / 21卷 / 05期

关键词：

The minimum sum-of-squares clustering problem; the k-means algorithm; incremental algorithm; DCA; global solution; local solution; convergence; GLOBAL K-MEANS; OPTIMIZATION ALGORITHM;

D O I：

暂无

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Solution methods for the minimum sum-of-squares clustering (MSSC) problem are analyzed and developed in this paper. Based on the DCA (Difference-of-Convex functions Algorithms) in DC programming and recently established qualitative properties of the MSSC problem [11], we suggest several improvements of the incremental algorithms of Ordin and Bagirov [26] and of Bagirov [4]. Properties of the new algorithms are obtained and preliminary numerical tests of those on real-world databases are shown. Finite convergence, convergence, and the rate of convergence of solution methods for the MSSC problem are presented for the first time in our paper. This Part 2 presents the incremental DC clustering algorithm of Bagirov and the three modified versions we suggest for it.

引用

页码：1109 / 1135

页数：27

共 29 条

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