The Seeding Algorithm for Spherical k-Means Clustering with Penalties

被引：1

作者：

Ji, Sai ^{[1
]}

Xu, Dachuan ^{[1
]}

Guo, Longkun ^{[2
]}

Li, Min ^{[3
]}

Zhang, Dongmei ^{[4
]}

机构：

[1] Beijing Univ Technol, Dept Operat Res & Sci Comp, Beijing 100124, Peoples R China

[2] Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350116, Fujian, Peoples R China

[3] Shandong Normal Univ, Sch Math & Stat, Jinan 250014, Peoples R China

[4] Shandong Jianzhu Univ, Sch Comp Sci & Technol, Jinan 250101, Peoples R China

来源：

ALGORITHMIC ASPECTS IN INFORMATION AND MANAGEMENT, AAIM 2019 | 2019年 / 11640卷

基金：

中国国家自然科学基金;

关键词：

Approximation algorithm; Spherical k-means clustering; Penalty;

D O I：

10.1007/978-3-030-27195-4_14

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Spherical k-means clustering is a generalization of k-means problem which is NP-hard and has widely applications in data mining. It aims to partition a collection of given data with unit length into k sets so as to minimize the within-cluster sum of cosine dissimilarity. In this paper, we introduce the spherical k-means clustering with penalties and give a 2 max{2, M}(1 + M)(ln k + 2)-approximate algorithm, where M is the ratio of the maximal and the minimal penalty values of the given data set.

引用

页码：149 / 158

页数：10

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