**Logic Learning Machine**- Flagship proprietary training and modeling algorithm for producing conditional logic-base predictive models.

**Decision Tree**- Proprietary construction algorithm that improves the C4.5 approach to achieve better rule models.

**Uniclassifier****®**- Revolutionary proprietary one-class training algorithm for unsupervised anomaly detection and rules discovery.

*K*-Nearest Neighbor- Proprietary enhancements enable treatment of both ordered and categorical variables through a suitable balance between distances.

**Neural Networks**- Proprietary optimizations enable training that achieves better models in less time.

**Logic Learning Machine**- Flagship proprietary training and modeling algorithm for producing conditional logic-based predictive models.

**Piecewise Linear**- Proprietary training algorithm that reconstructs a piecewise linear model from data, where different behaviors in the domain space are triggered by the value of categorical variables.

**Auto Regressive**- Proprietary preprocessing and postprocessing to identify linear models, especially for period identification. Considered Croston models are not standard and allow a better forecast of spiking time series, such as demand series.

*K*-Nearest Neighbor- Proprietary enhancements enable treatment of both ordered and categorical variables through a suitable balance between distances.

**Neural Networks**- Proprietary optimizations enable training that achieves better models in less time.

**Label Clustering**- Proprietary algorithm that adopts the modified
*K*-means technique of Standard Clustering for grouping patterns labeled by categorical variables. It can be used for predicting a vector of values instead of single ones, as in Regression models.

- Proprietary algorithm that adopts the modified
**Standard Clustering**- Proprietary enhancements enable treatment of both ordered and categorical variables through a suitable balance between distances.

**Projection Clustering**- Proprietary algorithm that adopts a modified
*K*-means technique for grouping patterns to allow projection on selected variables and produce a clustering in each of them.

- Proprietary algorithm that adopts a modified

**Rule Optimization**- Proprietary algorithm that optimizes rule-based models built with Logic Learning Machine or Decision Tree algorithms, while respecting constraints chosen by the user.

**Discretize**- Proprietary ADID (Attribute Driven Incremental Discretization) algorithm for a near-optimal discretization of ordered variables in a classification problem.

**Join**- Proprietary implementation of non-standard join functions for processing key relationships. In addition to
*less than*,*greater than*, and*different from*operators, it handles relationships between strings, phonetic and soft operators for faster text processing.

- Proprietary implementation of non-standard join functions for processing key relationships. In addition to
**Moving Window**- Proprietary algorithm enables static models like Classification and Regression to analyze and forecast time series.

**Reshape To Long**- Very useful non-standard task for unrolling data according to designated variables.

**Reshape To Wide**- Very useful non-standard task for performing the opposite operation of Reshape To Long.