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

[1] ON SOME INCREMENTAL ALGORITHMS FOR THE MINIMUM SUM-OF-SQUARES CLUSTERING PROBLEM. PART 1: ORDIN AND BAGIROV'S INCREMENTAL ALGORITHM
Tran Hung Cuong
Yao, Jen-Chih
Nguyen Dong Yen
JOURNAL OF NONLINEAR AND CONVEX ANALYSIS, 2019, 20 (08) : 1591 - 1608
[2] New and efficient DCA based algorithms for minimum sum-of-squares clustering
Le Thi Hoai An
Le Hoai Minh
Pham Dinh Tao
PATTERN RECOGNITION, 2014, 47 (01) : 388 - 401
[3] Analysis of gene expression profiles: an application of memetic algorithms to the minimum sum-of-squares clustering problem
Merz, P
BIOSYSTEMS, 2003, 72 (1-2) : 99 - 109
[4] Analysis of global k-means, an incremental heuristic for minimum sum-of-squares clustering
Hansen, P
Ngai, E
Cheung, BK
Mladenovic, N
JOURNAL OF CLASSIFICATION, 2005, 22 (02) : 287 - 310
[5] Analysis of Global k-Means, an Incremental Heuristic for Minimum Sum-of-Squares Clustering
Pierre Hansen
Eric Ngai
Bernard K. Cheung
Nenad Mladenovic
Journal of Classification, 2005, 22 : 287 - 310
[6] Minimum Sum-of-Squares Clustering by DC Programming and DCA
An, Le Thi Hoai
Tao, Pham Dinh
EMERGING INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE, 2009, 5755 : 327 - +
[7] Parameterized Algorithms for the 2-Clustering Problem with Minimum Sum and Minimum Sum of Squares Objective Functions
Wu, Bang Ye
Chen, Li-Hsuan
ALGORITHMICA, 2015, 72 (03) : 818 - 835
[8] Parameterized Algorithms for the 2-Clustering Problem with Minimum Sum and Minimum Sum of Squares Objective Functions
Bang Ye Wu
Li-Hsuan Chen
Algorithmica, 2015, 72 : 818 - 835
[9] Design of hybrids for the minimum sum-of-squares clustering problem
Pacheco, J
Valencia, O
COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2003, 43 (02) : 235 - 248
[10] Design of hybrids for the minimum sum-of-squares clustering problem
Pacheco, Joaquín
Valencia, Olga
Computational Statistics and Data Analysis, 2003, 43 (02): : 235 - 248

← 1 2 3 →