Online Clustering Algorithms for Semantic-Rich Network Trajectories

Abstract

With the advent of ubiquitous computing, a massive amount of trajectory data has been published and shared in many websites. This type of computing also provides motivation for online mining of trajectory data, to tit user-specific preferences or context (e.g., time of the day). While many trajectory clustering algorithms have been proposed, they have typically focused on offline mining and do not consider the restrictions of the underlying road network and selection conditions representing user contexts. In clear contrast, we study an efficient clustering algorithm for Boolean + Clustering queries using a pre-materialized and summarized data structure. Our experimental results demonstrate the efficiency and effectiveness of our proposed method using real-life trajectory data.