New life for your data

We’re passionate about data, and helping organizations get the greatest value out of their most precious resource. Explore our website to learn about UNCRi — our new computational framework for ML. 


About Us

Despite the enormous recent progress in AI and machine learning (ML), the reality is that most organizations must deal with tabular data consisting of a mix of numeric and categorical attributes, often with highly skewed distributions and missing values. Such datasets continue to pose challenges, and it is in this space that we are focused.
     At Skanalytix we develop methods and tools that allow organizations to better leverage their mixed-type data and enhance their data analytics capabilities by allowing insights not capable of being drawn using existing methods. Whether your focus is on data security and intrusion detection, data privacy, recommender systems, or countless other data-centric tasks, if you deal with complex mixed-type data then there is a place in your organization for our unique data modeling and inferencing framework.

UNCRi – A New Computational Framework for ML

Skanalytix has developed a new graph-based computational framework for ML named Unified Numerical-Categorical Representation and Inference (UNCRi), at the heart of which lies a unique data representation scheme coupled with a powerful inference procedure that can be used to estimate the probability distribution of any target variable, conditional on the values of one or more other variables. This flexibility allows a wide variety of generic data-oriented tasks to be performed, including predictiondata imputationjoint probability estimation and synthetic data generation. But these out-of-the-box solutions only scratch the surface. The framework’s breadth and flexibility allow it to easily extend to custom tasks such as the development of recommender systems. The UNCRi framework opens a world of possibilities!