An Efficient Frequent Pattern Mining Algorithm for Data Stream
Mining frequent patterns from transaction database, time series and data stream is an importa nt task now. Last decade, there are mainly two kinds of algorithms on frequent pattern mining. One is Apriori based on generating and testing, the other is FP growth based on dividing and conquering, which has been widely used in static data mining. But with the new requirements of data mining, mining frequent pattern is not restricted in the static datasets any more. For data stream, the frequent pattern mining algorithms must have strong ability of updating and adjusting to further improve its efficiency. This paper proposes a novel structure NC-Tree (New Compact Tree), which can recode and filter original data to compress dataset. At the same time, a new frequent pattern mining algorithm is introduced base on it, which can update and adjust the tree more efficiently. The experiments show the structure and algorithm obviously improves mining efficiency and ensures high accuracy.
Liu Hualei Lin Shukuan Qiao Jianzhong Yu Ge Lu Kaifu
College of Information Science and Engineering, Northeastern University, Shengyang 110004, Liaoning, China
国际会议
长沙
英文
757-761
2008-10-20（万方平台首次上网日期，不代表论文的发表时间）