Transparent Clustering with Cyclic Probabilistic Causal Models

Transparent Clustering with Cyclic Probabilistic Causal Models Full article

Journal

Studies in Computational Intelligence
ISSN: 1860-949X , E-ISSN: 1860-9503

Output data

Year: 2022, Volume: 1014, Pages: 239-253 Pages count : 15 DOI: 10.1007/978-3-030-93119-3_9

Authors

Vityaev E.E. ¹ , Pak B. ²

Affiliations

1	Sobolev Institute of Mathematics of SО RAS, 4 Acad. Koptyug avenue, Novosibirsk, Russian Federation
2	Novosibirsk State University, Novosibirsk, Russian Federation

Funding (2)

1	Sobolev Institute of Mathematics	0314-2019-0002
2	Russian Foundation for Basic Research	19-01-00331

In the previous work data clusters where discovered and visualized by causal models, used in cognitive science. Centers of clusters are presented by prototypes of clusters, formed by causal models, in accordance with the prototype theory of concepts, explored in cognitive science. In this work we describe the system of transparent analysis of such clasterization that bring the light to the interconnection between (1) set of objects with there characteristics (2) probabilistic causal relations between objects characteristics (3) causal models—fixpoints of probabilistic causal relations that form prototypes of clusters (4) clusters—set of objects that defined by prototypes. For that purpose we use a novel mathematical apparatus—probabilistic generalization of formal concepts—for discovering causal models via cyclical causal relations (fixpoints of causal relations). This approach is illustrated with a case study. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Vityaev E.E. , Pak B.
Transparent Clustering with Cyclic Probabilistic Causal Models
Studies in Computational Intelligence. 2022. V.1014. P.239-253. DOI: 10.1007/978-3-030-93119-3_9 Scopus РИНЦ OpenAlex

Scopus:	2-s2.0-85131822076
Elibrary:	48719367
OpenAlex:	W4285263297

Пока нет цитирований