传感器网络分布式数据流的频繁项集挖掘算法

研究无线传感器网络中数据流频繁项集挖掘问题。针对集中式的静态数据流频繁项集挖掘方法不能在传感器网络中直接使用这一特点，提出基于传感器网络的分布式数据流的频繁项集挖掘算法FIMVS。该算法基于FPtree快速挖掘出传感器节点上单一数据流的局部频繁项集，然后通过路由将其在无线传感器网络里逐层上传合并，在Sink节点上汇聚后，采用自顶向下的高效剪枝策略挖掘出全局频繁项集。实验结果表明，该算法能有效地大幅度减少候选项集，降低无线传感器网络中的通信量，并有较高的时间和空间效率。

Frequent Itemsets Mining Algorithm Based on Distributed Data Stream of Sensor Network

This paper mainly studied data stream frectuent itemsets mining problem of wireless sensor network. Aiming at the characteristics of sensor networks that centralized static data stream frequent itemset mining method cannot be directly used in sensor network,a frectuent itemset mining algorithm FIMDS based on distributed data stream of sensor network was proposed. Basai on FP-tree, the algorithm can fast mine the single data stream local frequent Itemsets of sensor nodes, and then through the routing, the local frequent itemsets arc uploaded and combined layer-by-layer, and last local frectuent itemsets collected on the sink node and global frequent itemsets are got by the top-down efficient pruning strategy. The experimental results show that the algorithm can effectively and greatly reduce candidate itemsets, and reduces the amount of communication traffic in wireless sensor networks, so the algorithm has good performance in time and spice.