Mining Frequent Item Sets with Multiple Convertible Constraints
带有多个可转化约束的频繁项集挖掘算法

Recent work has highlighted the importance of the constraint-based mining paradigm in the context of frequent item sets, associations, correlations. Some research has raised the notion "convertible constraints", and this method can push some constraints into the mining algorithm which essentially can't be pushed. This article has introduced a multiple convertible constraints mining algorithm, which analyzes the constraints by taking advantage of a sample database and then decides a optimal method to process data mining