【摘要】 提出根据信息熵划分属性值区间或集合、自动生成与人机交互相结合确定层次结构的方法,将多维多层多数据类型问题转化为受约束的一维单层布尔型问题.在此基础上,对直接生成频繁模式的FPT Gen算法进行了扩展,实现了有效挖掘多维多层关联规则的新算法MDML FPT Gen,其效率与可伸缩性均优于经典方法.更多还原

【Abstract】 Association rule discovery plays an important role in data mining. Most of the proposed algorithms are based on Apriori that scans databases as many times as the maximal length of patterns, which results in low efficiency in mining multidimension multilevel rules where the length of patterns over 20 is not uncommon. Moreover, current approaches deal with quantitative attributes by merging adjacent ranges to create simple concept hierarchies, which is too simple to be useful in real applications. To address these problems, a method based on the information entropy to partition quantitative intervals or qualitative values is presented in this paper. The automatic and interactive combined approach for the concept hierarchy formation is proposed. On the basis of that, multidimension multilevel multidatatype association rules can be mined by constrained singledimension singlelevel boolean algorithms. Discussions about FPTGen, an algorithm we proposed recently for mining frequent patterns, are detailed. The design of a new algorithm MDMLFPTGen, derived from FPTGen, is presented. Experimental evaluations show MDMLFPTGen is more efficient and scalable than Aprioribased classical algorithms.更多还原