摘要
最大频繁项集挖掘是许多数据挖掘应用中的重要问题.提出一种新的深度优先搜索最大频繁项集的算法.该算法采用位图数据格式,结合了流行的各种有效剪枝技术,并使用局部最大频繁项集来进行高效的超集存在判断,明显地加速了最大频繁项集的生成,从而降低了CPU时间.
Maximal frequent itemsets mining is a fundamental and important problem in many data mining applications. Since the MaxMiner algorithm first introduced the enumeration tree for MFI mining in 1998, there have been several proposed methods using depth-first search to improve performance. Here presented is DFMfi, a new depth-first search algorithm for mining maximal frequent itemsets. DFMfi adopts bitmap data format, several popular prune techniques which prune the search space efficiently, and local maximal frequent itemsets for superset checking quickly. Experimental comparison with the previous work indicates that it accelerates the generation of maximal frequent itemsets obviously, thus reducing CPU time.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2005年第3期462-467,共6页
Journal of Computer Research and Development
基金
国家自然科学基金项目(9010402660073001)国家"八六三"高技术研究发展计划基金项目(2002AA144040)
关键词
最大频繁项集
深度优先搜索
位图
前瞻剪枝
maximal frequent itemsets
depth-first search
bitmap
look-ahead pruning
