一种基于多维集的关联模式挖掘算法

大多数维间关联规则挖掘算法如基于数据立方体的关联规则挖掘算法都假定对象的属性取值只具有单值性．将对象的属性取值扩展到多值，据此提出多维集的概念和基于多维集关联规则的语义特征．在此语义特征下，提出了一个多维集的关联规则挖掘算法．该算法利用多维集关联规则的限制特征，能够在数据集缩减的同时进行侯选集的三重剪枝，因此，具有比直接使用apriori等算法更好的性能，分析了算法的性能和正确性、完备性，并通过实验对算法有效性进行了对比．

An Association Rule Mining Algorithm of Multidimensional Sets

Most of multidimensional association rule mining algorithms such as mining algorithms based on data cube assume that an object attribute only has a single-value. In this paper, the attribute value of an object is extended to a multi-value and the concept of multidimensional set is presented, which brings about the semantics of multidimensional set association rule. Based on the semantics, an algorithm to mine association rules of multidimensional sets is given. The algorithm makes use of the restricted characteristics of multidimensional set association rule and can execute a triplicate pruning of candidate sets with a reduction of data set, which makes it a better performance than that of apriori and other algorithms. The performance, correctness and completeness of the algorithm are analyzed, and its effectiveness is also proved by experiments.