A General Approach to Probabilistic Data Mining

0531047 20240103224239.620200720235959.9 85089240614 10.1007/978-981-15-4917-5_25 A General Approach to Probabilistic Data Mining 13 s. P cav_un_epca*0531043978-981-15-4916-8Sensor Networks and Signal Processingvol. 176Smart Innovation, Systems and Technologies325-340SingaporeSpringer2021PengSheng-LungFavorskayaMargarita N.ChaoHan-Chieh approximation probability models conditional independence decomposition information content ambiguity cav_un_auth*0101118 Jiroušek Radim UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory 50 Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0216188 Kratochvíl Václav UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory CZ 50 Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2020/MTR/kratochvil-0531047.pdf GA19-06569S GA ČR CZ cav_un_auth*0380559 MOST-04-18 Akademie věd - GA AV ČR CZ cav_un_auth*0393867 The paper describes principles enabling us to express the knowledge hidden in a multidimensional probability distribution - a distribution that is assumed to have generated the input data - into the form legible by humans, into the form expressible in a plain language. The generality of this approach arises from the fact that we do not assume any type of probability distribution. The basic idea is that the analysis of such a multidimensional distribution is, because of its computational complexity, intractable, and therefore we construct its approximation in a form of a decomposable model, which provides an easy interpretation. The process should be controlled by an expert in the field of application, and the presented principles give him instruction, how, using the tools from probability and information theories, to get satisfactory results. cav_un_auth*0393865 Sensor Networks and Signal Processing (SNSP 2019) /2./ 20191119 Hualien TW 20191122 IN 10000 10200 10201 2022 2 PR RVO:67985556 http://hdl.handle.net/11104/0310093 S 2021 85089240614 SCOPUS PUBMED WOS cav_un_epca*0531043 Sensor Networks and Signal Processing Smart Innovation, Systems and Technologies vol. 176 Springer 2021 Singapore 325 340 978-981-15-4916-8 2190-3018 Peng Sheng-Lung 340 Favorskaya Margarita N. 340 Chao Han-Chieh 340