AdvancedMiner

Main features:

• Performing a wide range of operations on data, such as sampling, joining datasets, dividing into testing/training/validating sets, assigning roles to attributes
• Graphical and interactive data exploration
• Outlier filtering, supplying missing values, PCA, various data transformations, etc.
• Building association models, clustering analyses, variable importance analyses, etc.
• Constructing various analytical models with the use of diverse Data Mining and statistical algorithms (such as classification trees, neuron networks, linear and logistic regression, K-means)
• Creation of scoring code so that the models can be integrated with other IT applications (scoring code may include the models as well as data transformations)
• Model quality evaluation and comparison of Data Mining models (LIFT, ROK, K-S, Confusion Matrix)
• Generation of model quality reports (MS Office)

Complex analytical processes can be defined in a simple way using drag & drop technique. Advanced users can create their own scripts and new node types.

AdvancedMiner offers limitless, additional funcionalities for advanced users that can be easily created and/or extended within the application.

Advanced features:

• Support for SQL language (including analytical functions)
• Integration with the R package
• Integration with Java and Hadoop Hive