关键词: 人工神经网络, 分布式多维数据流, 频繁模式, 挖掘算法, FR-tree 算法

Abstract: In the research of distributed multidimensional data stream frequent pattern mining algorithm, the non frequent items in multidimensional data stream are not deleted, and there is a problem of long average processing time. A distributed multidimensional data stream frequent pattern mining algorithm based on artificial neural network is proposed. According to the characteristics of artificial neural network, this method establishes an artificial neural network model and trains multi-dimensional data flow, so as to improve the mining efficiency; Based on the training results, a frequent pattern information tree, FR-tree ( Frequent Pattern tree ), is constructed. Because there are many expired multidimensional data streams in fr tree, it is necessary to prune fr tree and delete non frequent itemsets, so as to speed up the calculation of frequent patterns. Then, the distributed mining algorithm is used to mine the global fr tree to obtain the complete set of frequent itemsets of multidimensional data streams, so as to realize the mining of frequent patterns of distributed multidimensional data streams. The experimental results show that the average processing time of the method is tested to verify the practicability of the method.

Key words: artificial neural network, distributed multi-dimensional data flow, frequent patterns, mining algorithm, frequent pattern tree(FR-tree)