基于规模压缩的关联规则数据挖掘算法研究

Research on Algorithms of Association Rules Data Mining Based on Reduction Scale

基于关联规则的数据挖掘算法是人工智能和数据库研究的热点之一。本文提出的关联规则算法通过压缩规模,及时删除数据库中无用的事务记录,减少了事务数据的数量,提高了算法的执行效率。本算法能够生成较小规模的频繁候选集,有效减少了生成的候选集的规模,实现方便,在很大程度上也提高了效率。

Data mining based on association rules is one of the most active and new research in the field of artificial intelligence and database. Association rules are an important aspect of research of data mining. The new algorithms produces a more small amount of candidate patterns when it looks for the frequent patterns of the data base, using theory that the Parent pattern of the no- frequent pattern is no- freqent pattern. So it can reduce the scale of the candidatefrequent patterns greatly. It can be easily implemented, and can improve the efficiency greatly by deleting those useless records.