The application of ACO-based algorithms in data mining has been growing over the last few years, and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works about unsupervised learning have focused on clustering, showing the potential of ACO-based techniques. However, there are still clustering areas that are almost unexplored using these techniques, such as medoid-based clustering. Medoid-based clustering methods are helpful—compared to classical centroid-based techniques—when centroids cannot be easily defined. This paper proposes two medoid-based ACO clustering algorithms, where the only information needed is the distance between data: one algorithm that uses an ACO procedure to determine an optimal medoid set (METACOC algorithm) and another algorithm that uses an automatic selection of the number of clusters (METACOC-K algorithm). The proposed algorithms are compared against classical clustering approaches using synthetic and real-world datasets.

中文翻译：

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使用蚁群优化的基于类固醇的聚类

在过去的几年中，基于ACO的算法在数据挖掘中的应用一直在增长，并且使用这种以生物为灵感的方法已经开发了几种有监督和无监督的学习算法。关于无监督学习的最新作品都集中在聚类上，显示了基于ACO的技术的潜力。但是，仍然存在使用这些技术几乎无法开发的聚类区域，例如基于类固醇的聚类。与传统的基于质心的技术相比，当无法轻松定义质心时，基于质心的聚类方法会有所帮助。本文提出了两种基于medoid的ACO聚类算法，其中唯一需要的信息是数据之间的距离：一种算法使用ACO程序来确定最佳类固醇集（METACOC算法），另一种算法使用自动选择簇数（METACOC-K算法）。使用合成的和真实的数据集，将提出的算法与经典聚类方法进行了比较。