Continuous clustering trajectory stream of moving objects

The clustering of trajectories over huge volumes of streaming data has been recognized as critical for many modern applications. In this work, we propose a continuous clustering of trajectories of moving objects over high speed data streams, which updates online trajectory clusters on basis of incremental line-segment clustering. The proposed clustering algorithm obtains trajectory clusters efficiently and stores all closed trajectory clusters in a bi-tree index with efficient search capability. Next, we present two query processing methods by utilising three proposed pruning strategies to fast handle two continuous spatio-temporal queries, threshold-based trajectory clustering queries and threshold-based trajectory outlier detections. Finally, the comprehensive experimental studies demonstrate that our algorithm achieves excellent effectiveness and high efficiency for continuous clustering on both synthetic and real streaming data, and the proposed query processing methods utilise average 90% less time than the naive query methods.