Frequent Itemsets Summarization based on Neural Network

摘要：

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.

关键词： frequent itemsets neural network cluster restoration error

作者: ZHAO Zhikai QIAN Jiansheng CHENG Jian LU Nannan

作者单位: School of Computer Science and Technology China University of Mining and Technology Xuzhou, China School of Information and Electrical Engineering China University of Mining and Technology Xuzhou, C

会议类型: 国际会议

会议名称: 2009 2nd IEEE International Conference on Computer Science and Information Technology(第二届计算机科学与信息技术国际会议 ICCSIT2009)

会议地点: 北京

会议语种:英文

页码: 496-499

在线出版日期: 2009-08-08（万方平台首次上网日期，不代表论文的发表时间）