Frequent itemsets summarization based on neural network

In this paper, we propose a neural network and cluster based method K-ANN-FP to summarize the frequent itemsets to solve the interpretability obstacle of the large number of frequent itemsets. This method assume that the item exit in each frequent itemsets or not to contribute a Boolean matrix, then take the matrix and the corresponding frequency vectors to train the net. We use cluster to shorten the training time and keep the total restoration in a small threshold. We take the experiment on two UCI datasets; the result shows that the proposed method has fine effect both on the restoration error and the running time.