Bayesian Networks for Data Mining

By David E. Heckerman. Data mining and knowledge discovery, 2004

673 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A causal mapping approach to constructing Bayesian networks (2004, methodology): “The data-based approaches outline procedures by which Bayesian networks can be constructed using data [ 1 ].”
Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming (2006, methodology): “Such “freedom of expression” allows the researcher to explore complex relationships among the variables to discover new knowledge, making BNs an ideal tool for data mining (Heckerman 1997).”
Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners (2002, background): “GIOHMMs are Bayesian networks and therefore the general propagation and learning algorithms for Bayesian networks can be applied to them (Pearl, 1988; Lauritzen, 1996; Heckerman, 1997, 1998).”
Data Matching (2012, background): “Bayesian networks [138] can model selected dependencies between attributes.”
Large engineering project risk management using a Bayesian belief network (2009, methodology): “This study used unrestricted multinomial distribution based on Heckerman (1997). If a Bayesian belief network is constructed, sensitivity analysis is capable of analyzing how much a specific node is influenced by other nodes.”