论文标题
缠结：经验科学中人工智能的结构性方法（第一部分）
Tangles: a structural approach to artificial intelligence in the empirical sciences (Part I)
论文作者
论文摘要
传统聚类标识具有某些品质的对象组。缠结有相反：他们确定经常在一起发生的品质群体。因此，他们可以发现，关联和结构类型：行为，政治观点，文本或病毒。 如果需要，缠结也可以用作传统聚类的新方法。他们提供了一个特别适合模糊集群的精确，定量的范式，因为它们不需要对这些集体形成的群集的任何对象分配。 这是一本书的四个部分中的第一个，上述标题。该书探讨了从图形纠缠中概括的缠结概念和理论以外的数学探讨的应用。
Traditional clustering identifies groups of objects that share certain qualities. Tangles do the converse: they identify groups of qualities that often occur together. They can thereby discover, relate, and structure types: of behaviour, political views, texts, or viruses. If desired, tangles can also be used as a new method for traditional clustering. They offer a precise, quantitative paradigm suited particularly to fuzzy clusters, since they do not require any assignment of objects to the clusters which these collectively form. This is the first of four parts of a book with the above title. The book explores applications outside mathematics of the notion and theory of tangles generalised from the graph tangles know from graph minor theory.
