[Home ] [Archive]    
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Registration ::
:: Volume 8, Issue 2 (5-2017) ::
IJOR 2017, 8(2): 2-24 Back to browse issues page
A DC Optimization Algorithm for Clustering Problems with $𝑳_𝟏$-norm
Adil Bagirov Dr. , Sona Taheri Dr.
Federation University Australia , a.bagirov@federation.edu.au
Abstract:   (535 Views)
Clustering problems with the similarity measure defined by the $𝐿_1$-norm are studied. Characterizations of different stationary points of these problems are given using their difference of convex representations. An algorithm for finding the Clarke stationary points of the clustering problems is designed and a clustering algorithm is developed based on it. The clustering algorithm finds a center of a data set at the first iteration and gradually adds one cluster center at each consecutive iteration. The proposed algorithm is tested using large real world data sets and compared with other clustering algorithms.
Keywords: Cluster analysis, Nonsmooth optimization, Smoothing techniques, Incremental algorithm.
Full-Text [PDF 1142 kb]   (299 Downloads)    
Type of Study: Original | Subject: Continuous Optimization
Received: 2018/05/28 | Accepted: 2018/05/28 | Published: 2018/05/28
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA code



XML     Print



Volume 8, Issue 2 (5-2017) Back to browse issues page
مجله انجمن ایرانی تحقیق در عملیات Iranian Journal of Operations Research
Persian site map - English site map - Created in 0.06 seconds with 31 queries by YEKTAWEB 3764