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...
محفوظ في:
| المؤلفون الرئيسيون: | , , , , , |
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| التنسيق: | فصل الكتاب |
| اللغة: | English English |
| منشور في: |
Springer
2017
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| الموضوعات: | |
| الوصول للمادة أونلاين: | 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 http://umpir.ump.edu.my/id/eprint/16627/ http://dx.doi.org/10.1007/978-3-319-51281-5_23 |
| الوسوم: |
إضافة وسم
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| المؤسسة: | Universiti Malaysia Pahang Al-Sultan Abdullah |
| اللغة: | English English |
| الملخص: | 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. |
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