Tracing significant association rules using critical least association rules model. (1st January 2013)

• Record Type: Journal Article
• Authors: Abdullah, Zailani; Herawan, Tutut; Deris, Mustafa Mat
• Is Part Of: International journal of innovative computing and applications. Volume 5:Number 1(2013)
• Page Start: 3
• Page End: 17
• Publication Date: 2013-01-01
• Subjects: least association rules -- efficient -- critical; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Mining of least association rules from large databases has received a great attention in knowledge discovery. These rules are very useful especially in tracing the unexceptional events or situations. Until this moment, the ratio of studies in this area is still unbalanced as compared to mine frequent rules. The difficulties level of mining these rules as compared to frequent rules are different since it involves with the excessive in computational costs, rather complicated and entailed a dedicated measurement. Therefore, this paper proposed an efficient model called critical least association rule (CLAR) to mine the significant rules so called critical least association rules. Several experiments with real and UCI datasets has shown that the CLAR successfully in producing the critical least association rules, up to 1.5 times faster and less 96% complexity than benchmarked FP-growth algorithm.