摘要
挖掘频繁项集是挖掘数据流的基本任务。许多近似算法能够对数据流进行频繁项集的挖掘,但不能有效控制内存资源消耗和挖掘运行时间。为了提高数据流挖掘的效率,通过挖掘数据流中的频繁闭项集来减少挖掘结果项集的数量,并借鉴Relim算法和Manku算法,引入事务链表组作为概要数据结构,提出了一种新的数据流频繁闭项集的挖掘算法。最后通过实验,证明了该算法的有效性。
Mining frequent itemsets is a basic task of the data stream mining. Recently many approximate algorithms can mine frequent itemsets over data stream. However, these algorithms still cannot efficiently reduce space and time cost. To improve the efficiency, mining frequent close itemsets over data stream is proposed to reduce the number of frequent itemsets. Referring to the algorithms of Relim and Manku, the transaction list group is imported as the synopsis data structure, and a new algorithm of mining frequent close itemsets is put forward. At the end, experiments are done to prove the efficiency of this algorithm.
出处
《计算机工程与设计》
CSCD
北大核心
2008年第8期1896-1899,共4页
Computer Engineering and Design
关键词
数据流
数据挖掘
频繁项集
频繁闭项集
事务链表组
data stream
data mining
frequent itemsets
frequent close itemsets
transaction list group
