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.

中文翻译：

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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很多应用。我们提出了三种算法来为最小平方和聚类问题的改进算法创建起始解决方案。我们在文献中研究的 72 个实例上测试算法。我们找到了五个新的最知名的解决方案，并为其余 67 个实例中的 66 个匹配了最知名的解决方案。因此，我们能够证明，良好的起始解决方案与简单的本地搜索相结合，可以获得与文献中使用的更复杂算法相当的结果，有时甚至更好。