It is customary to split clustering analysis into an optimization level, then a (preferably) graphical representation level to take benefit of human vision for an effective understanding of (big) data structure. This article aspires to clarify relationships between clustering, both its process and its representation, and the underlying structural graph properties, both algebraic and geometric, starting from the mere knowledge of a dissimilarity matrix among items, possibly with missing entries. It is inspired by an analogous work on seriation problem, relating Robinson property in a dissimilarity with missing entries, with interval graph recognition using a sequence of 4 lexicographic breadth first searches.

中文翻译：

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不同点的聚类分析：代数和几何表示的回顾

通常将聚类分析分为优化级别，然后是（最好）图形表示级别，以利用人类视觉来有效理解（大）数据结构。本文旨在阐明聚类、其过程及其表示与底层结构图属性（代数和几何）之间的关系，从仅仅了解项目之间的相异矩阵开始，可能缺少条目。它的灵感来自一个关于序列化问题的类似工作，将 Robinson 属性与缺失条目的不相似性联系起来，使用 4 个字典广度优先搜索的序列进行区间图识别。