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ELP-M2: An Efficient Model for Mining Least Patterns from Data Repository

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

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Abstract

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.

Item Type: Book Section
Uncontrolled Keywords: Model; Least patterns; Data mining; Efficient
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 11 Apr 2017 02:25
Last Modified: 16 Oct 2018 08:23
URI: http://umpir.ump.edu.my/id/eprint/16627
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