Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › глава/раздел › научная › Рецензирование
Transparent Clustering with Cyclic Probabilistic Causal Models. / Vityaev, Evgenii E.; Pak, Bayar.
Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery. ред. / Boris Kovalerchuk; Kawa Nazemi; Răzvan Andonie; Nuno Datia; Ebad Banissi. 1. ред. Springer Science and Business Media Deutschland GmbH, 2022. стр. 239-253 (Studies in Computational Intelligence; Том 1014).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › глава/раздел › научная › Рецензирование
}
TY - CHAP
T1 - Transparent Clustering with Cyclic Probabilistic Causal Models
AU - Vityaev, Evgenii E.
AU - Pak, Bayar
N1 - Funding Information: Acknowledgements The work is financially supported by the Russian Foundation for Basic Research 19-01-00331-a and also was carried out within the framework of the state contract of the Sobolev Institute of Mathematics (project no.0314-2019-0002) regarding theoretical results. Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85131822076&partnerID=8YFLogxK
UR - https://www.elibrary.ru/item.asp?id=48719367
UR - https://www.mendeley.com/catalogue/31471fcc-766c-34ef-843f-d979305ecc04/
U2 - 10.1007/978-3-030-93119-3_9
DO - 10.1007/978-3-030-93119-3_9
M3 - Chapter
AN - SCOPUS:85131822076
SN - 978-3-030-93118-6
SN - 978-3-030-93121-6
T3 - Studies in Computational Intelligence
SP - 239
EP - 253
BT - Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery
A2 - Kovalerchuk, Boris
A2 - Nazemi, Kawa
A2 - Andonie, Răzvan
A2 - Datia, Nuno
A2 - Banissi, Ebad
PB - Springer Science and Business Media Deutschland GmbH
ER -
ID: 36430487