A soft set approach for association rules mining

Herawan, Tutut and Mat Deris, Mustafa (2011) A soft set approach for association rules mining. Knowledge-Based Systems, 24 (1). pp. 186-195.

Full text not available from this repository.

Abstract

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 a 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 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.

Item Type:Article
Uncontrolled Keywords:association rules mining; maximal association rules mining; boolean-valued information systems; soft set theory; items co-occurence
Subjects:Q Science > QA Mathematics > QA75 Calculating machines > QA75.5 Electronic computers. Computer science
Divisions:Faculty of Computer Science and Information Technology > Department of Software Engineering
ID Code:3034
Deposited By:Normajihan Abd. Rahman
Deposited On:20 Feb 2013 14:58
Last Modified:22 Jan 2015 08:35

Repository Staff Only: item control page