Abstract Clustering is a powerful tool in exploratory data analysis that partitions a set of objects into clusters with the goal of maximizing the similarity of objects within each cluster. Due to the tendency of clustering algorithms to find suboptimal partitions of data, the approximation method Simulated Annealing (SA) has been used to search for near-optimal partitions. However, existing SA-based partitional clustering algorithms still settle to local optima. We propose a new SA-based clustering algorithm, the Simulated Annealing with Gaussian Mutation and Distortion Equalization algorithm (SAGMDE), which uses two perturbation methods to allow for both large and small perturbations in solutions. Our experiments on a diverse collection of data sets show that SAGMDE performs more consistently and yields better results than existing SA clustering algorithms in terms of cluster quality while maintaining a reasonable runtime. Finally, we use generative art as a visualization tool to compare various partitional clustering algorithms.

中文翻译：

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一种具有双重扰动聚类方法的模拟退火算法

摘要 聚类是探索性数据分析中的一个强大工具，它将一组对象划分为簇，目标是最大化每个簇内对象的相似性。由于聚类算法倾向于寻找数据的次优分区，近似方法模拟退火 (SA) 已被用于搜索接近最优的分区。然而，现有的基于 SA 的分区聚类算法仍然满足于局部最优。我们提出了一种新的基于 SA 的聚类算法，即高斯变异和失真均衡模拟退火算法 (SAGMDE)，它使用两种扰动方法来允许解决方案中的大扰动和小扰动。我们在各种数据集上的实验表明，在保持合理运行时间的同时，SAGMDE 在集群质量方面比现有 SA 聚类算法执行得更一致，并产生更好的结果。最后，我们使用生成艺术作为可视化工具来比较各种分区聚类算法。