关键词: 关联规则, 数据挖掘, 最大频繁项集, 频繁模式树, 降维

Abstract: These algorithms based on FP-tree, for mining maximal frequent pattern, have high performance but with many drawbacks. For example, they must recursively generate conditional FP-trees and many candidate maximum frequent itemsets. In order to overcome these drawbacks of the existing algorithms, an algorithm named Based on Dimensionality Reduction of Frequent Itemset (BDRFI) for mining maximal frequent patterns was put forward after the analysis of FPMax and DMFIA algorithms. The new algorithm was based on decreasing dimension of itemset. In order to enhance efficiency of superset checking, the algorithm used Digital Frequent Pattern Tree (DFP-tree) instead of FP-tree, and reduced the number of mining through prediction and pruning before mining. During the mining process, a strategy of decreasing dimension of frequent itemset was used to generate candidate frequent itemsets. The method not only reduced the number of candidate frequent itemsets but also can avoid creating conditional FP-tree separately and recursively. The experimental results show that the efficiency of BDRFI is 2-8 times as much as that of other similar algorithms.

Key words: association rule, data mining, maximum frequent itemset, Frequent Pattern tree (FP-tree), decreasing dimension