2M-SELAR: A Model for Mining Sequential Least Association Rules
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 whi...
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| Format: | Conference or Workshop Item |
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Springer
2019
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| _version_ | 1796993507315941376 |
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| author | Abdullah, Zailani Omer, Adam Tutut, Herawan Noraziah, Ahmad Mohd Saman, Md Yazid Hamdan, Abdul Razak |
| author_facet | Abdullah, Zailani Omer, Adam Tutut, Herawan Noraziah, Ahmad Mohd Saman, Md Yazid Hamdan, Abdul Razak |
| author_sort | Abdullah, Zailani |
| collection | UMP |
| description | 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. |
| first_indexed | 2024-03-06T12:35:01Z |
| format | Conference or Workshop Item |
| id | UMPir25658 |
| institution | Universiti Malaysia Pahang |
| last_indexed | 2024-03-06T12:35:01Z |
| publishDate | 2019 |
| publisher | Springer |
| record_format | dspace |
| spelling | UMPir256582021-05-11T01:21:35Z http://umpir.ump.edu.my/id/eprint/25658/ 2M-SELAR: A Model for Mining Sequential Least Association Rules Abdullah, Zailani Omer, Adam Tutut, Herawan Noraziah, Ahmad Mohd Saman, Md Yazid Hamdan, Abdul Razak QA75 Electronic computers. Computer science 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. Springer 2019 Conference or Workshop Item PeerReviewed 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 https://doi.org/10.1007/978-981-13-1799-6_10 |
| spellingShingle | QA75 Electronic computers. Computer science Abdullah, Zailani Omer, Adam Tutut, Herawan Noraziah, Ahmad Mohd Saman, Md Yazid Hamdan, Abdul Razak 2M-SELAR: A Model for Mining Sequential Least Association Rules |
| title | 2M-SELAR: A Model for Mining Sequential Least Association Rules |
| title_full | 2M-SELAR: A Model for Mining Sequential Least Association Rules |
| title_fullStr | 2M-SELAR: A Model for Mining Sequential Least Association Rules |
| title_full_unstemmed | 2M-SELAR: A Model for Mining Sequential Least Association Rules |
| title_short | 2M-SELAR: A Model for Mining Sequential Least Association Rules |
| title_sort | 2m selar a model for mining sequential least association rules |
| topic | QA75 Electronic computers. Computer science |
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