基于关联规则的数据挖掘算法研究

Research of Data Mining Algorithm Based on Association Rules

关联规则是数据挖掘的重要手段,它基于支持度、置信度等对规则进行筛选,生成有用的规则。关联规则反映了大量数据中项集之间的相互依存性和关联性。Apriori算法和FP-Growth算法是关联规则挖掘中的两个典型算法。本文阐述了这两种算法的基本思想、数据挖掘步骤,并讨论了它们的优缺点及差异。

The association rule is the important means for data mining.It is mostly based on the degrees of support and confidence for the choice of useful rule.The association rule reflects the dependability and relevance between large number data items.Apriori algorithm and FP-Growth algorithm are two classic algorithms of association rule mining.This paper describes the basic idea and data mining steps of these two algorithms,discusses their advantages and disadvantages,and compares the differences between the two algorithms.