A Parallel Algorithm of Mining Frequent Pattern on Uncertain Data Streams

At present, more and more data are generated every day and the actual application requirements for the mining algorithm efficiency have become higher. In such a situation, one of the hot research topics on the frequent pattern mining over uncertain data is the spatiotemporal efficiency improvement of mining algorithms. Aiming at solving the frequent pattern mining problems over dynamic uncertain data streams, based on the existing algorithm researches, the paper proposes a parallel mining approximation algorithm based on the MapReduce framework by combining a highly efficient algorithm for static data. If this algorithm is used to mine frequent patterns, all the frequent patterns can be mined from a sliding window by using MapReduce at most twice. In the experiments conducted for this paper, in most cases the frequent item set was accurately discovered after MapReduce is used once. The experiments have shown that the spatiotemporal efficiency of the algorithm proposed in this paper is much better than those of the other algorithms.