The growing area of Data Mining defines a general framework for the induction of models from databases. Bayesian Networks are a class of graphical models which are able to deal with uncertainty. Nowadays, they are among the most promising ones. This paper summarizes our work on recovering Bayesian Networks in the framework of Data Mining, by commenting and discussing the insight gained in developing a Bayesian Network induction module in an existing Data Mining tool, Data Surveyor
A Bayesian Network is a probabilistic graphical model that represents a set of variables and their p...
Bayesian networks are a popular mechanism for dealing with uncertainty in complex situations. They a...
Dependency graphs are models for representing probabilistic inter-dependencies among related concept...
The growing area of Data Mining defines a general framework for the induction of models from databas...
Abstract. A Bayesian network is a graphical model that encodes probabilistic relationships among var...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
Bayesian networks have become a widely used method in the modelling of uncertain knowledge. Owing to...
Data mining is a statistical process to extract useful information, unknown patterns and interesting...
Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various ...
The analysis of nominal data is often reduced to accumulation and description. Bayesian methods offe...
Abstract: The process of learning of Bayesian Networks is composed of the stages of learning of the ...
A Bayesian (belief) network is a representation of a probability distribution over a set of random v...
A Bayesian network is a graphical model that encodes probabilistic relationships among variables of ...
Bayesian networks are powerful tools for representing relations of dependence among variables of a d...
We examine a graphical representation of uncertain knowledge called a Bayesian network. The represen...
A Bayesian Network is a probabilistic graphical model that represents a set of variables and their p...
Bayesian networks are a popular mechanism for dealing with uncertainty in complex situations. They a...
Dependency graphs are models for representing probabilistic inter-dependencies among related concept...
The growing area of Data Mining defines a general framework for the induction of models from databas...
Abstract. A Bayesian network is a graphical model that encodes probabilistic relationships among var...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
Bayesian networks have become a widely used method in the modelling of uncertain knowledge. Owing to...
Data mining is a statistical process to extract useful information, unknown patterns and interesting...
Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various ...
The analysis of nominal data is often reduced to accumulation and description. Bayesian methods offe...
Abstract: The process of learning of Bayesian Networks is composed of the stages of learning of the ...
A Bayesian (belief) network is a representation of a probability distribution over a set of random v...
A Bayesian network is a graphical model that encodes probabilistic relationships among variables of ...
Bayesian networks are powerful tools for representing relations of dependence among variables of a d...
We examine a graphical representation of uncertain knowledge called a Bayesian network. The represen...
A Bayesian Network is a probabilistic graphical model that represents a set of variables and their p...
Bayesian networks are a popular mechanism for dealing with uncertainty in complex situations. They a...
Dependency graphs are models for representing probabilistic inter-dependencies among related concept...