In this paper, we propose a fully autonomous density-based clustering algorithm named ‘Ocean’, which is inspired by the oceanic landscape and phenomena that occur in it. Ocean is an improvement over conventional algorithms regarding both distance metric and the clustering mechanism. Ocean defines the distance between two categories as the difference in the relative densities of categories. Unlike existing approaches, Ocean neither assigns the same distance to all pairs of categories, nor assigns arbitrary weights to matches and mismatches between categories that can lead to clustering errors. Ocean uses density ratios of adjacent regions in multidimensional space to detect the edges of the clusters. Ocean is robust against clusters of identical patterns. Unlike conventional approaches, Ocean neither makes any assumption regarding the data distribution within clusters, nor requires tuning of free parameters. Empirical evaluations demonstrate improved performance of Ocean over existing approaches.

中文翻译：

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OCEAN：一种使用类别相对密度的非常规无参数聚类算法

在本文中，我们提出了一种名为“Ocean”的完全自主的基于密度的聚类算法，其灵感来自海洋景观和其中发生的现象。Ocean 是对距离度量和聚类机制的传统算法的改进。Ocean 将两个类别之间的距离定义为类别相对密度的差异。与现有方法不同，Ocean 既不为所有类别对分配相同的距离，也不为可能导致聚类错误的类别之间的匹配和不匹配分配任意权重。Ocean 使用多维空间中相邻区域的密度比来检测聚类的边缘。Ocean 对相同模式的集群具有鲁棒性。与传统方法不同，Ocean 既不对集群内的数据分布做任何假设，也不需要调整自由参数。实证评估表明，Ocean 的性能优于现有方法。