一种实时有效的蜂群模式挖掘算法

针对实时相关运动模式挖掘应用的需求,提出了一种实时地发现关闭蜂群模式的簇重组算法(CLUR).该算法维护一个候选蜂群模式列表,在每个时间戳采用基于密度的聚类算法对移动目标进行聚类,根据聚类结果组合所有的最大移动目标集,记录相应的时间集,然后构建候选蜂群模式,并更新到候选列表.算法给出了三种更新规则和一种插入规则,用于实现候选蜂群模式列表的更新,同时降低了候选列表的冗余度,提高了算法的效率.在每个时间戳结束时可通过关闭检测规则实时地发现当前时刻的关闭蜂群模式.在合成数据上的综合实验验证了CLUR算法的正确性、实时性和高效性,CLUR算法适用于实时相关运动模式挖掘系统.

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.