A Group Mining Method for Big Data on Distributed Vehicle Trajectories in WAN

A distributed parallel clustering method MCR-ACA is proposed by integrating the ant colony algorithm with the computing framework Map-Combine-Reduce for mining groups with the same or similar features from big data on vehicle trajectories stored in Wide Area Network. The heaviest computing burden of clustering is conducted in parallel at local nodes, of which the results are merged to small size intermediates. The intermediates are sent to the central node and clusters are generated adaptively. The great overhead of transferring big volume data is avoided by MCR-ACA, which improves the computing efficiency and guarantees the correctness of clustering. MCR-ACA is compared with an existing parallel clustering algorithm on practical big data collected by the traffic monitoring system of Jiangsu province in China. Experimental results demonstrate that the proposed method is effective for group mining by clustering.