A soft set approach for association rules mining

作者：Herawan Tutut; Dens Mustafa Mat^*

来源：Knowledge-Based Systems, 2011, 24(1): 186-195.

DOI：10.1016/j.knosys.2010.08.005

• Association rules mining
• Maximal association rules mining
• Boolean-valued information systems
• Soft set theory
• Items co-occurrence

In this paper, we present an alternative approach for mining regular association rules and maximal association rules from transactional datasets using soft set theory. This approach is started by a transformation of a transactional dataset into a Boolean-valued information system. Since the "standard" soft set deals with such information system, thus a transactional dataset can be represented as a soft set. Using the concept of parameters co-occurrence in a transaction, we define the notion of regular and maximal association rules between two sets of parameters, also their support, confidence and maximal support, maximal confidences, respectively properly using soft set theory. The results show that the soft regular and soft maximal association rules provide identical rules as compared to the regular and maximal association rules.

• 出版日期2011-2