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Efficient implementation of compositional models for data mining
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SYSNO ASEP 0497540 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Efficient implementation of compositional models for data mining Author(s) Kratochvíl, Václav (UTIA-B) RID, ORCID
Jiroušek, Radim (UTIA-B) ORCID
Lee, T. R. (TW)Number of authors 3 Source Title Proceedings of the 21st Czech-Japan Seminar od Data Analysis and Decision Making. - Japan : Aoyama Gakuin University, Japan, 2018 / Sung Shao-Chin ; Vlach Milan - ISBN 978-80-7464-932-5 Pages s. 80-87 Number of pages 8 s. Publication form Print - P Action The 21st Czech-Japan Seminar on Data Analysis and Decision Making Event date 23.11.2018 - 26.11.2018 VEvent location Kamakura Country JP - Japan Event type WRD Language eng - English Country JP - Japan Keywords data mining ; mutual information ; compositional models ; conditional independence ; probability theory Subject RIV IN - Informatics, Computer Science OECD category Automation and control systems R&D Projects GA16-12010S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 Annotation A compositional model encodes probabilistic relationships among variables of interest. In connection with various statistical techniques, it represents a practical tool for data modeling and data mining. Structure of the model represents (un)conditional independencies among all variables. Relationships of dependent variables are described by low-dimensional probability distributions. Having a compositional model, a data miner can easily apply an intervention on variables of interest, fix values of other variables (conditioning), or to narrow the context of a problem (marginalization). The model learning process can be controlled to avoid overfitting of data.
In this paper, we present a new semi-supervised web application that will enable researchers to design probabilistic (compositional) models (both causal and stochastic). Thanks to the web architecture of the system, the researchers will always have a possibility to influence the data-based model construction process from any place of the world. It is also expected that the application of this methodology to practical problems will open new problems that will be an inspiration for further theoretical research.Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2019
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