【作者】 杨君锐； 赵群礼； 杜建；

【Author】 YANG Jun-Rui~1,ZHAO Qun-Li~1,and DU Jian~2 1(Department of Computer Science,Xi’an University of Science & Technology,Xi’an 710054) 2(School of Automobile,Chang’an University,Xi’an 710064)

【机构】 西安科技大学计算机科学与技术系； 长安大学汽车学院；

【摘要】 挖掘最大频繁项集在多种数据挖掘应用中有着重要的作用.目前,关于最大频繁项集的挖掘问题已经提出了一些算法,但是对于最大频繁项集维护问题的研究却很少.针对最大频繁项集的更新问题,提出了一个能够解决当最小支持度发生变化后在交易数据库中进行增量挖掘的IUMF(incremental updating maximum frequent itemsets)算法.更多还原

【Abstract】 Mining maximum frequent itemsets plays an important role in many data mining applications, and researchers present some algorithms for mining maximum frequent itemsets so far.However,very little work has been done for maintenance of mining maximum frequent itemsets.In this paper,an incremental mining algorithm(IUMF) is proposed for maintenance of mining maximum frequent itemsets based on changeable minimum support in an unchangeable database.The algorithm IUMA can effectively consider the attributes of maximum frequent itemsets and incremental mining,and the components are classified according to the dimension of the maximum frequent itemsets before the incremental mining,and then the incremental mining for mining maximum frequent itemsets is processed from then components of high dimension to the components of low dimension.The algorithm can be fit for the incremental mining of maximum frequent itemsets in large databases.更多还原