The domain of cluster analysis is a meeting point for a very rich multidisciplinary encounter, with cluster‐analytic methods being studied and developed in discrete mathematics, numerical analysis, statistics, data analysis, data science, and computer science (including machine learning, data mining, and knowledge discovery), to name but a few. The other side of the coin, however, is that the domain suffers from a major accessibility problem as well as from the fact that it is rife with division across many pretty isolated islands. As a way out, the present paper offers a thorough and in‐depth review of the clustering domain as a whole under the form of an outline map based on an overarching conceptual framework and a common language. With this framework we wish to contribute to structuring the clustering domain, to characterizing methods that have often been developed and studied in quite different contexts, to identifying links between methods, and to introducing a frame of reference for optimally setting up cluster analyses in data‐analytic practice.This article is categorized under: Technologies > Structure Discovery and Clustering

中文翻译：

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概念轮廓图的出现，以掌握聚类分析的丛林


聚类分析领域是非常丰富的多学科交叉的交汇点，聚类分析方法正在离散数学、数值分析、统计学、数据分析、数据科学和计算机科学（包括机器学习、数据挖掘）中研究和开发。和知识发现），仅举几例。然而，硬币的另一面是，该域名面临着主要的可访问性问题，而且它在许多相当孤立的岛屿上充斥着分裂。作为一种出路，本文以基于总体概念框架和通用语言的轮廓图的形式对整个聚类领域进行了彻底和深入的回顾。通过这个框架，我们希望有助于构建聚类领域，描述经常在不同背景下开发和研究的方法，识别方法之间的联系，并引入一个参考框架，以便在数据中最佳地设置聚类分析。分析实践。本文分类为：技术 > 结构发现和聚类