With a large number of distance measures, the appropriate choice for clustering a given data set with a specified clustering algorithm becomes an important problem. In this article, an automatic distance measure recommendation method for clustering algorithms is proposed. The recommendation method consists of the following steps: (1) metadata extraction, including meta-feature collection and meta-target identification; (2) recommendation model construction using metadata; and (3) distance measure recommendation for a new data set by the recommendation model. Two different types of meta-targets and meta-learning techniques are utilized considering the possible different requirements of users. To validate the necessity and effectiveness of the distance measure recommendation method, an empirical study is conducted with 199 publicly available data sets, 9 distance measures, and 2 widely used clustering algorithms. The experimental results indicate that distance measure significantly influences the performance of the clustering algorithm for a given data set. Furthermore, performance analysis of the proposed recommendation method proves its effectiveness.

中文翻译：

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聚类算法距离度量的自动推荐

With a large number of distance measures, the appropriate choice for clustering a given data set with a specified clustering algorithm becomes an important problem. 本文提出了一种用于聚类算法的自动距离度量推荐方法。推荐方法包括以下步骤： (1) 元数据提取，包括元特征收集和元目标识别；(2) 使用元数据构建推荐模型；(3)推荐模型对新数据集的距离度量推荐。考虑到用户可能的不同需求，使用了两种不同类型的元目标和元学习技术。为了验证距离度量推荐方法的必要性和有效性，对 199 个公开可用的数据集、9 个距离度量和 2 个广泛使用的聚类算法进行了实证研究。实验结果表明，距离度量显着影响给定数据集的聚类算法的性能。此外，所提出的推荐方法的性能分析证明了它的有效性。