NDLI: Bayesian based subgroup discovery

Content Provider	IEEE Xplore Digital Library
Author	Anwar, T. Asghar, S. Fong, S.
Copyright Year	2011
Description	Author affiliation: Department of Computer and Information Science, University of Macau, Macau SAR (Fong, S.) \|\| Center of Research in Data, Engineering (CORDE), Mohammad Ali Jinnah University, Islamabad, Pakistan (Anwar, T.) \|\| Department of Computer Science Mohammad Ali Jinnah University, Islamabad, Pakistan (Asghar, S.)
Abstract	Data Mining is concerned with extraction of interesting patterns or knowledge from huge amounts of Data. Generally data mining tasks are either predictive or descriptive. Classification falls under predictive induction while clustering and association rule mining fall under descriptive induction. Subgroup discovery is a task at the intersection of supervised learning and descriptive induction. In subgroup discovery we want to uncover individual patterns in data with a given property of interest. We want to find subgroups that cover a large population and are statistically different. The main application areas of subgroup discovery are exploration and descriptive induction, where the user wants to find the overview of dependencies between a target and many explaining variables. Many techniques have been proposed for discovering subgroups and some of these techniques are based on classification. But none of the techniques uses Bayesian networks for the generation of subgroups. Our contributions include a technique for the discovery of subgroups where the subgroups are generated using Bayesian networks.
Starting Page	154
Ending Page	161
File Size	1101494
Page Count	8
File Format	PDF
ISBN	9781457715389
e-ISBN	9781457715396
DOI	10.1109/ICDIM.2011.6093343
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-26
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian Networks Accuracy Databases Bayesian methods Subgroup Discovery Classification algorithms Data mining Mathematical model Equations
Content Type	Text
Resource Type	Article

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