This paper introduces a new algorithm of mining association rules. The algorithm RP counts the itemsets with different sizes in the same pass of scanning over the database by dividing the database into m partitions. ...This paper introduces a new algorithm of mining association rules. The algorithm RP counts the itemsets with different sizes in the same pass of scanning over the database by dividing the database into m partitions. The total number of passes over the database is only (k + 2m - 2)/m, where k is the longest size in the itemsets. It is much less than k.展开更多
In this paper, the problem of discovering association rules between items in a large database of sales transactions is discussed, and a novel algorithm, BitMatrix, is proposed. The proposed algorithm is fundamentally ...In this paper, the problem of discovering association rules between items in a large database of sales transactions is discussed, and a novel algorithm, BitMatrix, is proposed. The proposed algorithm is fundamentally different from the known algorithms Apriori and AprioriTid. Empirical evaluation shows that the algorithm outperforms the known ones for large databases. Scale-up experiments show that the algorithm scales linearly with the number of transactions.展开更多
挖掘最大频繁项目集是多种数据挖掘应用中的关键问题,之前的很多研究都是采用Apriori类的候选项目集生成-检验方法.然而,候选项目集产生的代价是很高的,尤其是在存在大量强模式和/或长模式的时候.提出了一种快速的基于频繁模式树(FP-tr...挖掘最大频繁项目集是多种数据挖掘应用中的关键问题,之前的很多研究都是采用Apriori类的候选项目集生成-检验方法.然而,候选项目集产生的代价是很高的,尤其是在存在大量强模式和/或长模式的时候.提出了一种快速的基于频繁模式树(FP-tree)的最大频繁项目集挖掘DMFIA(discover maximum frequent itemsets algorithm)及其更新算法UMFIA(update maximum frequent itemsets algorithm).算法UMFIA将充分利用以前的挖掘结果来减少在更新的数据库中发现新的最大频繁项目集的费用.展开更多
文摘This paper introduces a new algorithm of mining association rules. The algorithm RP counts the itemsets with different sizes in the same pass of scanning over the database by dividing the database into m partitions. The total number of passes over the database is only (k + 2m - 2)/m, where k is the longest size in the itemsets. It is much less than k.
基金This work was supported in part by the National '863' High-Tech Programme of China !(No.863-306-ZD06-2)
文摘In this paper, the problem of discovering association rules between items in a large database of sales transactions is discussed, and a novel algorithm, BitMatrix, is proposed. The proposed algorithm is fundamentally different from the known algorithms Apriori and AprioriTid. Empirical evaluation shows that the algorithm outperforms the known ones for large databases. Scale-up experiments show that the algorithm scales linearly with the number of transactions.
文摘挖掘最大频繁项目集是多种数据挖掘应用中的关键问题,之前的很多研究都是采用Apriori类的候选项目集生成-检验方法.然而,候选项目集产生的代价是很高的,尤其是在存在大量强模式和/或长模式的时候.提出了一种快速的基于频繁模式树(FP-tree)的最大频繁项目集挖掘DMFIA(discover maximum frequent itemsets algorithm)及其更新算法UMFIA(update maximum frequent itemsets algorithm).算法UMFIA将充分利用以前的挖掘结果来减少在更新的数据库中发现新的最大频繁项目集的费用.
