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Transparent Clustering with Cyclic Probabilistic Causal Models. / Vityaev, Evgenii E.; Pak, Bayar.

Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery. ed. / Boris Kovalerchuk; Kawa Nazemi; Răzvan Andonie; Nuno Datia; Ebad Banissi. 1. ed. Springer Science and Business Media Deutschland GmbH, 2022. p. 239-253 (Studies in Computational Intelligence; Vol. 1014).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research › peer-review

Harvard

Vityaev, EE & Pak, B 2022, Transparent Clustering with Cyclic Probabilistic Causal Models. in B Kovalerchuk, K Nazemi, R Andonie, N Datia & E Banissi (eds), Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery. 1 edn, Studies in Computational Intelligence, vol. 1014, Springer Science and Business Media Deutschland GmbH, pp. 239-253. https://doi.org/10.1007/978-3-030-93119-3_9

APA

Vityaev, E. E., & Pak, B. (2022). Transparent Clustering with Cyclic Probabilistic Causal Models. In B. Kovalerchuk, K. Nazemi, R. Andonie, N. Datia, & E. Banissi (Eds.), Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery (1 ed., pp. 239-253). (Studies in Computational Intelligence; Vol. 1014). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-93119-3_9

Vancouver

Vityaev EE, Pak B. Transparent Clustering with Cyclic Probabilistic Causal Models. In Kovalerchuk B, Nazemi K, Andonie R, Datia N, Banissi E, editors, Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery. 1 ed. Springer Science and Business Media Deutschland GmbH. 2022. p. 239-253. (Studies in Computational Intelligence). doi: 10.1007/978-3-030-93119-3_9

Author

Vityaev, Evgenii E. ; Pak, Bayar. / Transparent Clustering with Cyclic Probabilistic Causal Models. Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery. editor / Boris Kovalerchuk ; Kawa Nazemi ; Răzvan Andonie ; Nuno Datia ; Ebad Banissi. 1. ed. Springer Science and Business Media Deutschland GmbH, 2022. pp. 239-253 (Studies in Computational Intelligence).

BibTeX

@inbook{29cd7e2439844b24ac749a78a50d0472,
title = "Transparent Clustering with Cyclic Probabilistic Causal Models",
abstract = "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.",
author = "Vityaev, {Evgenii E.} and Bayar Pak",
note = "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: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2022",
doi = "10.1007/978-3-030-93119-3_9",
language = "English",
isbn = "978-3-030-93118-6",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "239--253",
editor = "Boris Kovalerchuk and Kawa Nazemi and R{\u a}zvan Andonie and Nuno Datia and Ebad Banissi",
booktitle = "Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery",
address = "Germany",
edition = "1",

}

RIS

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.

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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