Supports all analytical tasks:
- Data extraction and transformation
- Building and eveluating models
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.
- Data preparation (ETL)
- Construction of data marts
- Credit scoring
- Customer segmentation and profiling
- Cross-selling and up-selling recomendations
- Market basket analysis
- Marketing campaigns optimization
- Churn analysis & prevention
- Customer Lifetime Value analysis
- Risk analysis
- Optimization of debt recovery process
- Validation and updating of existing scoring models
- Fraud detection
- Extracting and saving data from/to different database systems and files
- 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).
AdvancedMiner offers limitless, additional funcionalities for advanced users that can be easily created and/or extended within the application.
- Support for SQL language (including analytical functions)
- Integration with the R package
- Integration with Java and Hadoop Hive
See AdvancedMiner documentation