The Parallel Seeding Algorithm for k-Means Problem with Penalties

被引：0
|
作者：
Li, Min [1 ]
Xu, Dachuan [2 ]
Yue, Jun [1 ]
Zhang, Dongmei [3 ]
机构：
[1] Shandong Normal Univ, Sch Math & Stat, Jinan 250014, Peoples R China
[2] Beijing Univ Technol, Dept Operat Res & Sci Comp, Beijing 100124, Peoples R China
[3] Shandong Jianzhu Univ, Sch Comp Sci & Technol, Jinan 250101, Peoples R China
来源：
ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH | 2020年 / 37卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Approximation algorithm; k-means; penalty; seeding algorithm; parallel; APPROXIMATION ALGORITHMS;
D O I：
10.1142/S0217595920400059
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
As a classic NP-hard problem in machine learning and computational geometry, the k-means problem aims to partition a data point set into k clusters such that the sum of the squared distance from each point to its nearest center is minimized. The k-means problem with penalties, denoted by k-MPWP, generalizing the k-means problem, allows that some points can be paid some penalties instead of being clustered. In this paper, we study the seeding algorithm of k-MPWP and propose a parallel seeding algorithm for k-MPWP along with the corresponding theoretical analysis.
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页数：18
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