Efficient algorithm for real-time mining swarm patterns

Due to urgent demands for real time relative motion patterns mining applications, an efficient cluster-recombinant (CLUR) algorithm for real time discovering closed swarm patterns was proposed. The algorithm maintains a candidate swarm list, and at each timestamp carries out cluster analysis on moving objects using the clustering algorithm based on density, and according to the clustering results it recombines the maximum moving object set and records the corresponding maximum time set, further constructs a candidate swarm pattern and then finally updates the candidate swarm list up to date by using three update rules and an insert rule. The rules greatly reduce the redundancy of the candidate list and improve the efficiency of the algorithm. At the end of each timestamp, the current closed swarm patterns can be real time obtained by closuring checking rules. Comprehensive empirical studies on large synthetic data demonstrate the correctness, real time and efficiency of the CLUR algorithm. The CLUR algorithm can be applicable to real time relative motion pattern mining systems.