关键词: 数据挖掘, 最大频繁项集, 候选项集, 交集, 剪枝

Abstract: Discovering maximal frequent itemset is a key issue in data mining；to look for an algorithm that can avoid the generating of vast volume of candidate itemsets，or the generating of frequent pattern tree，an intersection pruning algorithm（IPA） is proposed to find the maximum frequent sets for itemset of all properties from the maximum frequent itemset for transaction itemset.It combines a top-down and bottom-up searches for maximum frequent itemset through intersection pruning，and uses the distribution data of properties and information of the generated intersections，etc. to reduce the number of intersects.Up to（1- minimum support）×|D|+1 intersections are calculated，so the time complexity of this algorithm is relatively low；experiments show that this algorithm is valid and efficient，and it is also easy in coding for use in KDD applications.

Key words: data mining, maximum frequent itemsets, candidate itemsets, intersection, pruning