The problem of association rule mining has gained considerable prominence in the data mining community for its use as an important tool of knowledge discovery from large scale databases. And there has been a spurt of...The problem of association rule mining has gained considerable prominence in the data mining community for its use as an important tool of knowledge discovery from large scale databases. And there has been a spurt of research activities around this problem. However, traditional association rule mining may often derive many rules in which people are uninterested. This paper reports a generalization of association rule mining called φ association rule mining. It allows people to have different interests on different itemsets that arethe need of real application. Also, it can help to derive interesting rules and substantially reduce the amount of rules. An algorithm based on FP tree for mining φ frequent itemset is presented. It is shown by experiments that the proposed methodis efficient and scalable over large databases.展开更多
高效用项集挖掘已成为关联规则中的一个热点研究问题.一些基于垂直结构的算法已用来挖掘高效用项集,此类算法的主要优点是将项集的事务和效用信息存储到效用列表中.在求一个项集的超集所在事务可以通过对它的子集进行一次交集运算得到....高效用项集挖掘已成为关联规则中的一个热点研究问题.一些基于垂直结构的算法已用来挖掘高效用项集,此类算法的主要优点是将项集的事务和效用信息存储到效用列表中.在求一个项集的超集所在事务可以通过对它的子集进行一次交集运算得到.这种算法在稀疏数据集中非常的有效.但在稠密数据集中存在一个问题,即列表中存储的事务太多,在计算用于剪枝的效用上界时,需要耗费大量的存储空间,同时也影响运行速度.并且在现有的算法中,缺乏针对稠密数据集的高效用项集挖掘算法,往往需要设置很高的最小效用阈值,影响算法的运行效率.针对此问题,提出一个新的算法D-HUI(mining High Utility Itemsets using Diffsets)以及一个新的数据结构—项集列表,首次在高效用项集挖掘中引入差集的概念.利用事务的差集求项集的效用上界,减少计算量以及存储空间,从而提高算法的运行效率.实验结果表明,提出的算法在稠密数据集中,执行速度更快,内存消耗更少.展开更多
文摘The problem of association rule mining has gained considerable prominence in the data mining community for its use as an important tool of knowledge discovery from large scale databases. And there has been a spurt of research activities around this problem. However, traditional association rule mining may often derive many rules in which people are uninterested. This paper reports a generalization of association rule mining called φ association rule mining. It allows people to have different interests on different itemsets that arethe need of real application. Also, it can help to derive interesting rules and substantially reduce the amount of rules. An algorithm based on FP tree for mining φ frequent itemset is presented. It is shown by experiments that the proposed methodis efficient and scalable over large databases.
文摘高效用项集挖掘已成为关联规则中的一个热点研究问题.一些基于垂直结构的算法已用来挖掘高效用项集,此类算法的主要优点是将项集的事务和效用信息存储到效用列表中.在求一个项集的超集所在事务可以通过对它的子集进行一次交集运算得到.这种算法在稀疏数据集中非常的有效.但在稠密数据集中存在一个问题,即列表中存储的事务太多,在计算用于剪枝的效用上界时,需要耗费大量的存储空间,同时也影响运行速度.并且在现有的算法中,缺乏针对稠密数据集的高效用项集挖掘算法,往往需要设置很高的最小效用阈值,影响算法的运行效率.针对此问题,提出一个新的算法D-HUI(mining High Utility Itemsets using Diffsets)以及一个新的数据结构—项集列表,首次在高效用项集挖掘中引入差集的概念.利用事务的差集求项集的效用上界,减少计算量以及存储空间,从而提高算法的运行效率.实验结果表明,提出的算法在稠密数据集中,执行速度更快,内存消耗更少.
