NJIT Researcher Michael Houle Proves Theory for Detecting Data Anomalies

In data analysis, it’s the outlier information that is usually the most interesting, yet sometimes that information goes unrecognized by the most common evaluation methods because they make inaccurate assumptions. 

Read More