This project introduces UN-AVOIDS, an unsupervised and nonparametric approach that combines in a coherent and seamless model both visualization (a human process) and detection (an algorithmic process) of outliers. The main aspect of novelty of UN-AVOIDS is that it transforms data into a new space called neighborhood cumulative density function (NCDF), in which both visualization and detection are carried out. In this space, outliers are remarkably visually distinguishable, and therefore the anomaly scores assigned by the detection algorithm achieved a high area under the ROC curve (AUC).  

Ongoing task consists of designing a visualization   aided anomaly detection (VAAD), a type of software that aids analysts by providing UN-AVOIDS’ detection algorithm (running in a back engine), NCDF visualization space (rendered to plots), along with other conventional methods of visualization in the original feature space, all of which are linked in one interactive environment.