摘要
Apriori算法已成为关联规则挖掘的一个经典方法,广泛地被应用于如贸易决策、银行信用评估、金融保险等诸多领域。这种自底向上方法挖掘短频繁项集时效果较好,当频繁项集较长时,其时间复杂度量呈指数增长态势。本文结合自顶向下和自底向上搜索两种方法,提出一种能更好解决长、短频繁项集问题的双向挖掘方法。通过计算复杂度分析的实验表明,所提出的方法是有效可行的。
The Apriori algorithm has become a classic method for mining association rules. It is widely applied to various fields such as trade decision- making, bank evaluating credit, finance insurance, etc. This method is an effective down- top algorithm for minging frequents, but it will come across time consuming huge computing problems in mining long pattern frequent itemsets（e, g. 100 items）. A new ideal method of top down mining frequent itemsets , which adopts some new concepts such as transaction and itemset association information tables, key items and reduction items,projection DB, etc is presented. It is very effective, especially when being used to mine long items. This paper propses a new method, combining the top down search method and bottom up search method , but its main search strategy is still top -down method. This algorithm can better solve problems of long＆short frequents, the validity and effectiveness of the proposed algorithm is proved through the analysis of computing complexity and experimentation.
出处
《计算机科学》
CSCD
北大核心
2006年第12期196-199,共4页
Computer Science
基金
上海市教委科研基金
上海市重点学科建设项目资助(编号:T0602)。
