论文标题
在最小正方形聚类问题中，良好起始解决方案的重要性
The Importance of Good Starting Solutions in the Minimum Sum of Squares Clustering Problem
论文作者
论文摘要
聚类问题在机器学习，操作研究和统计数据中有许多应用。我们提出了三种算法，以创建针对此问题改进算法的启动解决方案。我们测试了文献中研究的72个实例的算法。其中有48个相对容易解决，我们为所有人发现了很多次最著名的解决方案。二十四个中和大尺寸实例更具挑战性。我们找到了五种最著名的解决方案，并与剩下的19个实例中的18个实例中最著名的解决方案相匹配。
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 this problem. We test the algorithms on 72 instances that were investigated in the literature. Forty eight of them are relatively easy to solve and we found the best known solution many times for all of them. Twenty four medium and large size instances are more challenging. We found five new best known solutions and matched the best known solution for 18 of the remaining 19 instances.
