摘要
提出基于事务矩阵挖掘最大频繁项集的方法AFMI,该方法采取迭代精简事务矩阵的方式求解所有事务中的最大频繁项集,从精简后的事务向量交集的子集中搜索最大频繁项集,并运用逻辑运算和剪枝方法提高挖掘效率。基于AFMI方法,研究挖掘滑动窗口数据流最大频繁项集算法AFMI+,该算法可使用户周期性地挖掘当前窗口中的最大频繁项集。实验结果表明,AFMI和AFMI+算法均具有较好的性能。
A method called AFMI based on a transaction matrix is proposed to mine the maximum frequent itemsets. The frequent itemsets are obtained from all the transactions by means of condensing iteratively the transaction matrix, the transaction vector intersections are acquired to reduce the range of search. Logical operations and pruning methods are adopted to improve the efficiency of the mining. Based on AFMI, an algorithm called AFMI＋ is proposed, which can mine maximum frequent itemsets from a sliding window over data streams. AFMI＋ can get the maximum frequent itemsets in current sliding window over data streams just when users need to get them periodically. Experimental result shows that AFMI and AFMI＋ algorithms have better performance.
出处
《计算机工程》
CAS
CSCD
北大核心
2010年第22期86-87,90,共3页
Computer Engineering
基金
南京工业大学青年教师学术基金资助项目(39709013)
关键词
数据挖掘
数据流
滑动窗口
最大频繁项集
矩阵
data mining
data stream
sliding window
maximum frequent itemsets
matrix
