Subject: Release of BayesiaLab 4.2, the leading Bayesian Network software for Data Mining
Bayesia announces the release of
the version 4.2 of BayesiaLab, the leading software in the market of
Bayesian networks and Data Mining.
The main new features of this release are:
- Markov Blanket export in SAS macro. In order to ease the deployment of
the Bayesian scoring functions, in direct marketing applications for
example, this tool allows to export the Markov blanket of the target
variable in a SAS macro. This macro can then perform exact inference on
the target variable from the values of the Markov blanket's nodes.
- Variable Clustering. This algorithm uses the
graph structure of the Bayesian network and the forces of its arcs to
cluster the variables that are close semantically;
- Multiple Clustering. Allows to carry out data clustering on the classes
variables returned by the variable clustering algorithm. A latent
variable synthesizing the manifest variables is induced for each class.
It is then possible to discover the probabilistic
relations that hold between these latent variables, and, possibly, with
the manifest variables;
- Data Clustering. The
implementation of a new score and the development of a new search
strategy allow obtaining much more relevant clusters. These clusters
are more stable, and come with an
- Mapping tool allowing to have a graphical representation of the
- Batch Joint Probability. In order to detect outliers by
taking into account all the variables this tool
allows to compute the joint
probability of each record of a database;
- and, as usual, increased ergonomics of the interface and performance improvements.
You can consult the
newsletter that describes more in details the new functionalities of
download BayesiaLab 4.2 for a new 30 days evaluation period, and you can also have a
look at the
presentations that illustrate successful applications of BayesiaLab.
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