A new efficient algorithm based on DC programming and DCA for clustering

被引：49

作者：

An, Le Thi Hoai ^{[1
]}

Belghiti, M. Tayeb

Tao, Pham Dinh

机构：

[1] Univ Paul Verlaine Metz, Lab Theoret & Appl Comp Sci, UFR MIM, F-57045 Metz, France

[2] Inst Natl Sci Appl, Lab Modelling Optimizat & Operat Res, F-76131 Mont St Aignan, France

来源：

JOURNAL OF GLOBAL OPTIMIZATION | 2007年 / 37卷 / 04期

关键词：

clustering; K-median problem; K-means algorithm; DC programming; DCA; nonsmooth nonconvex programming;

D O I：

10.1007/s10898-006-9066-4

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 [运筹学与控制论]; 12 [管理学]; 1201 [管理科学与工程]; 1202 [工商管理学]; 120202 [企业管理];

摘要：

In this paper, a version of K-median problem, one of the most popular and best studied clustering measures, is discussed. The model using squared Euclidean distances terms to which the K-means algorithm has been successfully applied is considered. A fast and robust algorithm based on DC (Difference of Convex functions) programming and DC Algorithms (DCA) is investigated. Preliminary numerical solutions on real-world databases show the efficiency and the superiority of the appropriate DCA with respect to the standard K-means algorithm.

引用

页码：593 / 608

页数：16

共 44 条

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Alon N., 1991, The Probabilistic Method

[2]

A TABU SEARCH APPROACH TO THE CLUSTERING PROBLEM [J].