AN INCREMENTAL NONSMOOTH OPTIMIZATION ALGORITHM FOR CLUSTERING USING L1 AND L∞ NORMS

被引：0
作者：
Ordin, Burak [1 ]
Bagirov, Adil [2 ,3 ]
Mohebi, Ehsan [4 ]
机构：
[1] Ege Univ, Fac Sci, Dept Math, TR-35100 Izmir, Turkey
[2] Chongqing Normal Univ, Sch Math Sci, Chongqing, Peoples R China
[3] Federat Univ Australia, Sch Sci Engn & Informat Technol, Ballarat, Vic 3353, Australia
[4] Federat Univ Australia, Ballarat, Vic 3353, Australia
来源：
JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION | 2020年 / 16卷 / 06期
基金：
澳大利亚研究理事会;
关键词：
Nonsmooth optimization; nonconvex optimization; cluster analysis; unsupervised learning; incremental algorithm; K-MEANS ALGORITHM;
D O I：
10.3934/jimo.2019079
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
An algorithm is developed for solving clustering problems with the similarity measure defined using the L-1 and L(infinity )norms. It is based on an incremental approach and applies nonsmooth optimization methods to find cluster centers. Computational results on 12 data sets are reported and the proposed algorithm is compared with the X-means algorithm.
引用
收藏
页码：2757 / 2779
页数：23
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