ELP-M2: An Efficient Model for Mining Least Patterns from Data Repository
Most of the algorithm and data structure facing a computational problem when they are required to deal with a highly sparse and dense dataset. Therefore, in this paper we proposed a complete model for mining least patterns known as Efficient Least Pattern Mining Model (ELP-M2) with LP-Tree data stru...
| Main Authors: | , , , , , |
|---|---|
| Format: | Book Chapter |
| Language: | English English |
| Published: |
Springer
2017
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/16627/ http://umpir.ump.edu.my/id/eprint/16627/1/book%20chapter1.pdf http://umpir.ump.edu.my/id/eprint/16627/7/3.%20ELP%20M2%20An%20Efficient%20Model%20for%20Mining%20Least%20Patterns%20from%20Data%20Repository.pdf |
| _version_ | 1848820239220867072 |
|---|---|
| author | Zailani, Abdullah Amir, Ngah Herawan, Tutut Noraziah, Ahmad Siti Zaharah, Mohamad Abdul Razak, Hamdan |
| author2 | Herawan, Tutut |
| author_facet | Herawan, Tutut Zailani, Abdullah Amir, Ngah Herawan, Tutut Noraziah, Ahmad Siti Zaharah, Mohamad Abdul Razak, Hamdan |
| author_sort | Zailani, Abdullah |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Most of the algorithm and data structure facing a computational problem when they are required to deal with a highly sparse and dense dataset. Therefore, in this paper we proposed a complete model for mining least patterns known as Efficient Least Pattern Mining Model (ELP-M2) with LP-Tree data structure and LP-Growth algorithm. The comparative study is made with the well-know LP-Tree data structure and LP-Growth algorithm. Two benchmarked datasets from FIMI repository called Kosarak and T40I10D100K were employed. The experimental results with the first and second datasets show that the LP-Growth algorithm is more efficient and outperformed the FP-Growth algorithm at 14% and 57%, respectively. |
| first_indexed | 2025-11-15T02:06:17Z |
| format | Book Chapter |
| id | ump-16627 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T02:06:17Z |
| publishDate | 2017 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-166272018-10-16T08:23:26Z http://umpir.ump.edu.my/id/eprint/16627/ ELP-M2: An Efficient Model for Mining Least Patterns from Data Repository Zailani, Abdullah Amir, Ngah Herawan, Tutut Noraziah, Ahmad Siti Zaharah, Mohamad Abdul Razak, Hamdan Q Science (General) QA Mathematics Most of the algorithm and data structure facing a computational problem when they are required to deal with a highly sparse and dense dataset. Therefore, in this paper we proposed a complete model for mining least patterns known as Efficient Least Pattern Mining Model (ELP-M2) with LP-Tree data structure and LP-Growth algorithm. The comparative study is made with the well-know LP-Tree data structure and LP-Growth algorithm. Two benchmarked datasets from FIMI repository called Kosarak and T40I10D100K were employed. The experimental results with the first and second datasets show that the LP-Growth algorithm is more efficient and outperformed the FP-Growth algorithm at 14% and 57%, respectively. Springer Herawan, Tutut Rozaida, Ghazali Nazri, Mohd Nawi Mustafa, Mat Deris 2017 Book Chapter PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/16627/1/book%20chapter1.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/16627/7/3.%20ELP%20M2%20An%20Efficient%20Model%20for%20Mining%20Least%20Patterns%20from%20Data%20Repository.pdf Zailani, Abdullah and Amir, Ngah and Herawan, Tutut and Noraziah, Ahmad and Siti Zaharah, Mohamad and Abdul Razak, Hamdan (2017) ELP-M2: An Efficient Model for Mining Least Patterns from Data Repository. In: Recent Advances on Soft Computing and Data Mining: The Second International Conference on Soft Computing and Data Mining (SCDM-2016), Bandung, Indonesia, August 18-20, 2016 Proceedings. Advances in Intelligent Systems and Computing (AISC), 549 . Springer, Cham, pp. 224-232. ISBN 978-3-319-51279-2 http://dx.doi.org/10.1007/978-3-319-51281-5_23 doi: 10.1007/978-3-319-51281-5_23 |
| spellingShingle | Q Science (General) QA Mathematics Zailani, Abdullah Amir, Ngah Herawan, Tutut Noraziah, Ahmad Siti Zaharah, Mohamad Abdul Razak, Hamdan ELP-M2: An Efficient Model for Mining Least Patterns from Data Repository |
| title | ELP-M2: An Efficient Model for Mining Least Patterns from Data Repository |
| title_full | ELP-M2: An Efficient Model for Mining Least Patterns from Data Repository |
| title_fullStr | ELP-M2: An Efficient Model for Mining Least Patterns from Data Repository |
| title_full_unstemmed | ELP-M2: An Efficient Model for Mining Least Patterns from Data Repository |
| title_short | ELP-M2: An Efficient Model for Mining Least Patterns from Data Repository |
| title_sort | elp-m2: an efficient model for mining least patterns from data repository |
| topic | Q Science (General) QA Mathematics |
| url | http://umpir.ump.edu.my/id/eprint/16627/ http://umpir.ump.edu.my/id/eprint/16627/ http://umpir.ump.edu.my/id/eprint/16627/ http://umpir.ump.edu.my/id/eprint/16627/1/book%20chapter1.pdf http://umpir.ump.edu.my/id/eprint/16627/7/3.%20ELP%20M2%20An%20Efficient%20Model%20for%20Mining%20Least%20Patterns%20from%20Data%20Repository.pdf |