Visualization aided engagement pattern validation for big longitudinal web behavior intervention data

This paper proposes a visualization aided pattern validation to identify optimal number of clusters for big longitudinal web behavior intervention data. The proposed validation consists of two parts: The weighted validation index including overlap and separation measures, and visualization integrating a between-stress mapping and trajectory characterization. The proposed method is applied to a longitudinal web behavior intervention dataset and a set of simulated zero-inflated data using parameters from this web trial. Four engagement patterns for this web behavioral intervention are identified and validated using our proposed method.