0497538 20240111141011.420181204235959.9 12 s. P cav_un_epca*0497537978-80-7464-932-590-101JapanAoyama Gakuin University, Japan2018SungShao-ChinVlachMilan compositional model data mining conditional independence mutual information cav_un_auth*0101118 Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory 34 Jiroušek Radim UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0216188 Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory 33 Kratochvíl Václav UTIA-B CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0368377 33 Lee T. R. TW PDF http://library.utia.cas.cz/separaty/2018/MTR/jirousek-0497538.pdf cav_un_auth*0356801 MOST-18-04 GA AV ČR CZ 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. cav_un_auth*0368378 The 21st Czech-Japan Seminar on Data Analysis and Decision Making 20181123 20181126 Kamakura JP IN 20000 20200 20205 2019 3 PR RVO:67985556 http://hdl.handle.net/11104/0291221 S 2018 SCOPUS PUBMED WOS PDF cav_un_epca*0497537 Proceedings of the 21st Czech-Japan Seminar od Data Analysis and Decision Making Aoyama Gakuin University, Japan 2018 Japan 90 101 978-80-7464-932-5 340 Sung Shao-Chin 340 Vlach Milan