运筹学学报 ›› 2022, Vol. 26 ›› Issue (1): 99-112.doi: 10.15960/j.cnki.issn.1007-6093.2022.01.007

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带惩罚μ-相似Bregman散度k-均值问题的初始化算法

刘文杰1, 张冬梅1*,*(), 张鹏2, 邹娟3   

  1. 1. 山东建筑大学计算机科学与技术学院, 山东济南 250101
    2. 山东大学软件学院, 山东济南 250101
    3. 曲阜师范大学数学科学学院, 山东曲阜 273165
  • 收稿日期:2021-07-21 出版日期:2022-03-15 发布日期:2022-03-14
  • 通讯作者: 张冬梅 E-mail:zhangdongmei@sdjzu.edu.cn
  • 作者简介:张冬梅   E-mail: zhangdongmei@sdjzu.edu.cn
  • 基金资助:
    国家自然科学基金(11871081);国家自然科学基金(61972228);国家自然科学基金(11801310);山东省自然科学基金(ZR2021MF006)

The seeding algorithm for $\mu$-similar Bregman divergences $k$-means problem with penalties

Wenjie LIU1, Dongmei ZHANG1*,*(), Peng ZHANG2, Juan ZOU3   

  1. 1. School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, Shandong, China
    2. School of Software, Shandong University, Jinan 250101, Shandong, China
    3. School of Mathematical Sciences, Qufu Normal University, Qufu 273165, Shandong, China
  • Received:2021-07-21 Online:2022-03-15 Published:2022-03-14
  • Contact: Dongmei ZHANG E-mail:zhangdongmei@sdjzu.edu.cn

摘要:

$k$-均值问题是聚类中的经典问题,亦是NP-难问题。如果允许数据点不聚类,而是支付惩罚费用,则引出带惩罚的$k$-均值问题。本文将带惩罚的$k$-均值问题从欧氏距离推广到更一般的$\mu$-相似Bregman散度,研究了带惩罚$\mu$-相似Bregman散度$k$-均值问题的初始化算法。本文给出的初始化算法,近似比与$\mu$和数据点惩罚最大值与最小值的比例$r$相关。

关键词: 近似算法, $k$-均值, 惩罚, $\mu$-相似Bregman散度, 初始化算法

Abstract:

The $k$-means problem is a classic NP-hard problem in clustering analysis. If somedata points are allowed not to be clustered but to pay some penalty, the $k$-means problem with penalty is induced. In this paper, weextend the $k$-means problem with penalty from Euclidean distance tothe more general $\mu $-similar Bregman divergences, and study theinitialization algorithm for the $\mu $-similar Bregman divergences $k$-means problem with penalty. The seeding algorithm is given wherethe approximation ratio is only related to $\mu $ and the ratio ofthe maximum value to the minimum value of the data point penalty.

Key words: approximation algorithm, $k$-means, penalty, $\mu$-similar Bregman divergences, seeding algorithm

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