Compositional Models for Data Mining: an Example

0497538 - ÚTIA 2019 RIV JP eng C - Konferenční příspěvek (zahraniční konf.)
Jiroušek, Radim - Kratochvíl, Václav - Lee, T. R.
Compositional Models for Data Mining: an Example.
Proceedings of the 21st Czech-Japan Seminar od Data Analysis and Decision Making. Japan: Aoyama Gakuin University, Japan, 2018 - (Sung, S.; Vlach, M.), s. 90-101. ISBN 978-80-7464-932-5.
[The 21st Czech-Japan Seminar on Data Analysis and Decision Making. Kamakura (JP), 23.11.2018-26.11.2018]
Grant ostatní: GA AV ČR(CZ) MOST-18-04
Program: Bilaterální spolupráce
Institucionální podpora: RVO:67985556
Klíčová slova: compositional model * data mining * conditional independence * mutual information
Obor OECD: Automation and control systems
http://library.utia.cas.cz/separaty/2018/MTR/jirousek-0497538.pdf

Like Bayesian networks, compositional models may also be used for data mining. Nevertheless, one can find several reasons why to prefer compositional models for this purpose. Perhaps the most important is the fact that compositional models are assembled from low-dimensional (unconditional) distributions so that computationally advantageous formulas are known for information theoretic characteristics computation. The other reason is that a decomposition is a natural way of complex tasks simplification. Therefore, the inverse process of composition is easily understandable for specialists from many fields of applications regardless of their level of mathematical education.
Trvalý link: http://hdl.handle.net/11104/0291221