Pith. sign in

REVIEW 1 cited by

Tangles: a structural approach to artificial intelligence in the empirical sciences (Part I)

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2006.01830 v2 pith:W2MUDVPL submitted 2020-06-03 cs.AI math.CO

classification cs.AImath.CO
keywords tanglestheybookclusteringclustersgraphgroupsobjects
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Traits and tangles: An analysis of the Big Five paradigm by tangle-based clustering

    q-bio.NC 2024-11 conditional novelty 6.0 of 10

    Tangle clustering of 50 IPIP Big Five questions reveals 11 to 15 hierarchical personality traits, including the five OCEAN traits, which appear and disappear at different resolutions.

Pith tools