ONLINE DATA STREAM MINING OF RECENT FREQUENT ITEMSETS BASED ON SLIDING WINDOW MODEL

摘要：

Online data stream mining is one of the most important issues in data mining. Identifying the recent knowledge can provide valuable information for the analysis of the data stream. In this paper, we proposed an one-pass data stream mining algorithm to mine the recent frequent itemsets in data streams with a sliding window basing on transactions. To reduce the cost of time and memory needed to slide the windows, each items is denoted a bit-sequence representations. Basing on Apriori property, this kind of representations can find frequent items in data streams efficiently. We named this method MRFI-SW (Mining Recent Frequent Itemsets by Sliding Window) algorithm. Experiment results show that the proposed algorithm not only attains highly accurate mining result, but also consumes less memory than existing algorithms for mining frequent itemsets over recent data streams.

关键词： Online data stream Data mining Sliding windows

作者: JIA-DONG REN KE LI

作者单位: College of Information Science and Engineering YanShan University, Qinhuangdao 066004, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 293-298

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）