Visual Extraction of Interaction Patterns Guided by Hierarchical Clustering and Process Mining

Keywords

Pattern discovery in interaction logs, visual analytics, dynamic time warping, hierarchical clustering, process mining

Abstract

Understanding user interactions in digital systems is essential in analyzing user behaviors and improving system usability. However, a collection of interaction sequences is often large and unstructured, making it challenging to uncover interaction patterns. To address this challenge, we introduce a visual analytics approach that integrates hierarchical clustering and process mining techniques to support analysts in exploring unstructured, large interaction sequence data. Our system employs a tailored dynamic time warping-based similarity measure to enable comparison of interaction sequences. Based on the sequence similarities, we provide stepwise, interactive navigation of clustering results with contextual visual cues for refinement and validation. We further apply process mining to characterize derived clusters. Through these hierarchical clustering and process mining steps, analysts can progressively uncover meaningful interaction patterns while utilizing visual guidance and incorporating domain expertise. We demonstrate our system's effectiveness and applicability through two case studies involving system designers, developers, and domain experts.