Less is more: simple algorithms for the minimum sum of squares clustering problem

IF 1.9 3区工程技术 Q3 MANAGEMENT

IMA Journal of Management Mathematics Pub Date : 2021-08-19 DOI:10.1093/imaman/dpab031

P. Kalczynski, J. Brimberg, Z. Drezner

引用次数: 3

Abstract

The clustering problem has many applications in machine learning, operations research and statistics. We propose three algorithms to create starting solutions for improvement algorithms for the minimum sum of squares clustering problem. We test the algorithms on 72 instances that were investigated in the literature. We found five new best known solutions and matched the best known solution for 66 of the remaining 67 instances. Thus, we are able to demonstrate that good starting solutions combined with a simple local search get results comparable with, and sometimes even better than, more sophisticated algorithms used in the literature.

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少即是多:最小平方和聚类问题的简单算法

聚类问题在机器学习、运筹学和统计学中有许多应用。我们提出了三种算法来创建最小平方和聚类问题的改进算法的起始解。我们在文献中调查的72个实例上测试了算法。我们发现了五个新的最知名的解决方案，并为其余67个实例中的66个匹配了最知名的方案。因此，我们能够证明，良好的启动解决方案与简单的局部搜索相结合，所获得的结果与文献中使用的更复杂的算法相当，有时甚至更好。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IMA Journal of Management Mathematics OPERATIONS RESEARCH & MANAGEMENT SCIENCE-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

CiteScore

4.70

自引率

17.60%

发文量

审稿时长

>12 weeks

期刊介绍： The mission of this quarterly journal is to publish mathematical research of the highest quality, impact and relevance that can be directly utilised or have demonstrable potential to be employed by managers in profit, not-for-profit, third party and governmental/public organisations to improve their practices. Thus the research must be quantitative and of the highest quality if it is to be published in the journal. Furthermore, the outcome of the research must be ultimately useful for managers. The journal also publishes novel meta-analyses of the literature, reviews of the "state-of-the art" in a manner that provides new insight, and genuine applications of mathematics to real-world problems in the form of case studies. The journal welcomes papers dealing with topics in Operational Research and Management Science, Operations Management, Decision Sciences, Transportation Science, Marketing Science, Analytics, and Financial and Risk Modelling.