【摘要】 关联规则挖掘是数据挖掘中一个重要的模型．在其挖掘算法中,如果最小支持度很高,则出现频率比较低的规则就不能发现;如果最小支持度太低,因为频繁项的相互关联,则会出现组合爆炸．为此,提出了允许用户设定多个最小支持度、给定数据各项的权重来解决这一问题．理论、实验数据和实际应用证明,该新算法可行且符合实际情况,比同类算法用时更少,对大型数据库的关联规则挖掘非常有效．更多还原

【Abstract】 Association rules mining is an important model in data mining. In its mining algorithms, if minimum support (minsup) is set too high, rules that involve rare items will not be found. To find rules that involve both frequent and rare items, minsup has to be set very low. This may cause combinatorial explosion because those frequent items will be associated with another in all possible ways. A new algorithm is proposed to solve this problem. The algorithm allowes the user to specify multiple minimum supports and gives items weights to reflect the natures of the items and their varied frequencies in the database. And it is proved by the correlative theorem that the algorithm is feasible and experiment data reflect the practicability of the new method. Experiment results show that the new algorithm is very effective for large databases.更多还原