Mining non-redundant recurrent rules from a sequence database Online publication date: Thu, 15-Nov-2018
by SeungYong Yoon; Hirohisa Seki
International Journal of Computational Intelligence Studies (IJCISTUDIES), Vol. 7, No. 3/4, 2018
Abstract: Many methods have been studied for mining sequential patterns from a sequence database. In particular, Lo et al. have proposed the notion of recurrent rules and an algorithm called NR3 for mining them. Recurrent rules can express temporal constraints such as "Whenever a series of precedent events occurs, eventually a series of consequent events occurs", and they are useful in various domains, especially in software specification and verification. Although the algorithm NR3 and its successor BOB for mining non-redundant recurrent rules have been given by Lo et al. mining recurrent rules still requires considerable computational costs. In this paper, we propose a new algorithm, called LF-NR3, to make NR3 more efficient, which is based on a familiar program transformation loop fusion. We apply the loop fusion technique to NR3, thereby simplifying the operations of the original mining algorithm. We also make use of a hash-based data structure to reduce loads of the manipulation of sequences repeatedly required in mining recurrent rules. We show the effectiveness of our proposed method based on experimental results of some datasets used in the literature.
Online publication date: Thu, 15-Nov-2018
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Intelligence Studies (IJCISTUDIES):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org