The Spherical k-means++ Algorithm via Local Search - 文章详情页

作者：

Tian, Xiaoyun (Tian, Xiaoyun.) | Xu, Dachuan (Xu, Dachuan.) (学者：徐大川) | Du, Donglei (Du, Donglei.) | Gai, Ling (Gai, Ling.)

收录：

EI Scopus

摘要：

We　consider　the　spherical　k-means　problem　(SKMP),　a　generalization　of　the　k-means　clustering　problem　(KMP).　Given　a　data　set　of　n　points　(Formula　Presented)　in　d-dimensional　unit　sphere　(Formula　Presented),　and　an　integer　(Formula　Presented),　it　aims　to　partition　the　data　set　(Formula　Presented)　into　k　sets　so　as　to　minimize　the　sum　of　cosine　dissimilarity　measure　from　each　data　point　to　its　closest　center.　We　present　a　constant　expected　approximation　guarantee　for　this　problem　based　on　integrating　the　k-means++　seeding　algorithm　for　the　KMP　and　the　local　search　technique.　©　2020,　Springer　Nature　Switzerland　AG.

关键词：

Spheres Local search (optimization) Approximation algorithms K-means clustering

作者机构：

[ 1 ] [Tian, Xiaoyun]Department of Operations Research and Information Engineering, Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Xu, Dachuan]Department of Operations Research and Information Engineering, Beijing University of Technology, Beijing; 100124, China
[ 3 ] [Du, Donglei]Faculty of Management, University of New Brunswick, Fredericton; NB; E3B 9Y2, Canada
[ 4 ] [Gai, Ling]Glorious Sun School of Business and Management, Donghua University, Shanghai; 200051, China

通讯作者信息：

[gai, ling]glorious sun school of business and management, donghua university, shanghai; 200051, china

电子邮件地址：

lgai@dhu.edu.cn

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