数据流中频繁闭合模式的挖掘  

Frequent Closed Patterns Mining over Data Streams

作　　者：程转流[1] 胡学钢[1] 

机构地区：[1]合肥工业大学计算机与信息学院

出　　处：《计算机工程》2008年第16期50-52,共3页Computer Engineering

基　　金：安徽省高等学校自然科学基金资助项目(KJ2007B236);安徽省高等学校青年教师科研基金资助项目(2008jq1143)

摘　　要：频繁闭合模式集可唯一确定频繁模式完全集。根据数据流的特点,提出一种挖掘频繁闭合项集的算法,该算法将数据流分段,用DSFCI_tree动态存储潜在频繁闭合项集,对每一批到来的数据流,建立局部DSFCI_tree,进而对全局DSFCI_tree进行更新并剪枝,从而有效地挖掘整个数据流中的频繁闭合模式。实验表明,该算法具有良好的时间和空间效率。The set of frequent closed patterns uniquely determines the complete set of all frequent patterns. According to the features of data streams, a new algorithm is proposed for mining the frequent closed patterns. The data streams are partitioned into a set of segments, and a DSFCI tree is used to store the potential frequent closed patterns dynamically. With the arrival of each batch of data, the algorithm builds a corresponding local DSFCI tree, then updates and prunes the global DSFCI tree effectively to mine the frequent closed patterns in the entire data streams. The experiments and analysis show that the algorithm has good performance.

关 键 词：数据挖掘 数据流 关联规则 频繁闭合项集 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]