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.
CITATION STYLE
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 Advances in Intelligent Systems and Computing (Vol. 549 AISC, pp. 224–232). Springer Verlag. https://doi.org/10.1007/978-3-319-51281-5_23
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