Local Search Approximation Algorithms for the Spherical k-Means Problem

被引：0
|
作者：
Zhang, Dongmei [1 ]
Cheng, Yukun [2 ]
Li, Min [3 ]
Wang, Yishui [4 ]
Xu, Dachuan [5 ]
机构：
[1] Shandong Jianzhu Univ, Sch Comp Sci & Technol, Jinan 250101, Peoples R China
[2] Suzhou Univ Sci & Technol, Suzhou Key Lab Big Data & Informat Serv, Sch Business, Suzhou 215009, Peoples R China
[3] Shandong Normal Univ, Sch Math & Stat, Jinan 250014, Peoples R China
[4] Chinese Acad Sci, Shenzhen Inst Adv Technol, 1068 Xueyuan Ave, Shenzhen 518055, Peoples R China
[5] Beijing Univ Technol, Dept Operat Res & Sci Comp, Beijing 100124, Peoples R China
来源：
ALGORITHMIC ASPECTS IN INFORMATION AND MANAGEMENT, AAIM 2019 | 2019年 / 11640卷
关键词：
Spherical k-means; Local search; Approximation algorithm; FACILITY LOCATION; YIELDS;
D O I：
10.1007/978-3-030-27195-4_31
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper, we study the spherical k-means problem (SKMP) which is one of the most well-studied clustering problems. In the SKMP, we are given an n-client set D in d-dimensional unit sphere S-d, and an integer k <= n. The goal is to open a center subset F subset of S-d with vertical bar F vertical bar <= k that minimizes the sum of cosine dissimilarity measure for each client in D to the nearest open center. We give a (2(4 + root 7) + epsilon)-approximation algorithm for this problem using local search scheme.
引用
收藏
页码：341 / 351
页数：11
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