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