ELP-M2: An efficient model for mining least patterns from data repository

Zailani Abdullah, Amir Ngah, Tutut Herawan, Noraziah Ahmad, Siti Zaharah Mohamad, Abdul Razak Hamdan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationRecent Advances on Soft Computing and Data Mining - The 2nd International Conference on Soft Computing and Data Mining, SCDM-2016, Proceedings
PublisherSpringer Verlag
Pages224-232
Number of pages9
Volume549 AISC
ISBN (Print)9783319512792
DOIs
Publication statusPublished - 2017
EventThe 2nd International Conference on Soft Computing and Data Mining, SCDM-2016 - Bandung, Indonesia
Duration: 18 Aug 201620 Aug 2016

Publication series

NameAdvances in Intelligent Systems and Computing
Volume549 AISC
ISSN (Print)21945357

Other

OtherThe 2nd International Conference on Soft Computing and Data Mining, SCDM-2016
CountryIndonesia
CityBandung
Period18/8/1620/8/16

Fingerprint

Data structures

Keywords

  • Data mining
  • Efficient
  • Least patterns
  • Model

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Abdullah, Z., Ngah, A., Herawan, T., Ahmad, N., Mohamad, S. Z., & Hamdan, A. R. (2017). ELP-M2: An efficient model for mining least patterns from data repository. In Recent Advances on Soft Computing and Data Mining - The 2nd International Conference on Soft Computing and Data Mining, SCDM-2016, Proceedings (Vol. 549 AISC, pp. 224-232). (Advances in Intelligent Systems and Computing; Vol. 549 AISC). Springer Verlag. https://doi.org/10.1007/978-3-319-51281-5_23

ELP-M2 : An efficient model for mining least patterns from data repository. / Abdullah, Zailani; Ngah, Amir; Herawan, Tutut; Ahmad, Noraziah; Mohamad, Siti Zaharah; Hamdan, Abdul Razak.

Recent Advances on Soft Computing and Data Mining - The 2nd International Conference on Soft Computing and Data Mining, SCDM-2016, Proceedings. Vol. 549 AISC Springer Verlag, 2017. p. 224-232 (Advances in Intelligent Systems and Computing; Vol. 549 AISC).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abdullah, Z, Ngah, A, Herawan, T, Ahmad, N, Mohamad, SZ & Hamdan, AR 2017, ELP-M2: An efficient model for mining least patterns from data repository. in Recent Advances on Soft Computing and Data Mining - The 2nd International Conference on Soft Computing and Data Mining, SCDM-2016, Proceedings. vol. 549 AISC, Advances in Intelligent Systems and Computing, vol. 549 AISC, Springer Verlag, pp. 224-232, The 2nd International Conference on Soft Computing and Data Mining, SCDM-2016, Bandung, Indonesia, 18/8/16. https://doi.org/10.1007/978-3-319-51281-5_23
Abdullah Z, Ngah A, Herawan T, Ahmad N, Mohamad SZ, Hamdan AR. ELP-M2: An efficient model for mining least patterns from data repository. In Recent Advances on Soft Computing and Data Mining - The 2nd International Conference on Soft Computing and Data Mining, SCDM-2016, Proceedings. Vol. 549 AISC. Springer Verlag. 2017. p. 224-232. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-319-51281-5_23
Abdullah, Zailani ; Ngah, Amir ; Herawan, Tutut ; Ahmad, Noraziah ; Mohamad, Siti Zaharah ; Hamdan, Abdul Razak. / ELP-M2 : An efficient model for mining least patterns from data repository. Recent Advances on Soft Computing and Data Mining - The 2nd International Conference on Soft Computing and Data Mining, SCDM-2016, Proceedings. Vol. 549 AISC Springer Verlag, 2017. pp. 224-232 (Advances in Intelligent Systems and Computing).
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