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2M-SELAR: A Model for Mining Sequential Least Association Rules

Abdullah, Zailani and Omer, Adam and Tutut, Herawan and Noraziah, Ahmad and Mohd Saman, Md Yazid and Hamdan, Abdul Razak (2019) 2M-SELAR: A Model for Mining Sequential Least Association Rules. In: Proceedings of the International Conference on Data Engineering (DaEng-2015), 25-26 April 2015 , Bali, Indonesia. pp. 91-99., 520. ISBN 978-981-13-1799-6

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Recently, mining least association rule from the sequential data becomes more important in certain domain areas such as education, healthcare, text mining, etc. due to its uniqueness and usefulness. However, discovering such rule is a great challenge because it involves with a set of least items which usually holds a very low in term of support. Therefore, in this paper propose a model for mining sequential least association rule (2M-SELAR) that embedded with SELAR algorithm, and Critical Relative Support (CRS) and Definite Factor (DF) measures. The experimental results reveal that 2M-SELAR can successfully generate the desired rule from the given datasets.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Lecture Notes in Electrical Engineering
Uncontrolled Keywords: Sequential least association rules, Data mining, Education
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Civil Engineering & Earth Resources
Depositing User: Noorul Farina Arifin
Date Deposited: 11 May 2021 01:21
Last Modified: 11 May 2021 01:21
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