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Data mining with graphical models
Author(s) -
Rudolf Kruse,
Christian Borgelt
Publication year - 1998
Publication title -
lecture notes in computer science
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 0.249
H-Index - 400
eISSN - 1611-3349
pISSN - 0302-9743
DOI - 10.1007/bfb0095424
Subject(s) - computer science , exploit , graphical model , dependency (uml) , inference , data mining , probabilistic logic , knowledge extraction , artificial intelligence , machine learning , data science , computer security
The explosion of data stored in commercial or administrational databases calls for intelligent techniques to discover the patterns hidden in them and thus to exploit all available information. There-fore a new line of research has recently been established, which became known under the names “Data Mining” and “Knowledge Discovery in Databases”. In this paper we study a popular technique from its arsenal of methods to do dependency analysis, namely learning inference networks (also called “graphical models”) from data. We review the already well-known probabilistic networks and provide an introduction to the recently developed and closely related possibilistic networks.

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