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1860797082832470016
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INTELEK Repository
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to all authors
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2019-03-05 20:11:10
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Restricted Document
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11305
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UniSZA
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5522-01-FH02-FIK-20-38454.pdf
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Author
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oai_dc
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https://intelek.unisza.edu.my/intelek/pages/view.php?ref=11305
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11305 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=11305 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal application/pdf 4 Adobe Acrobat Pro DC 20 Paper Capture Plug-in 1.7 Author to all authors to all authors 2019-03-05 20:11:10 5522-01-FH02-FIK-20-38454.pdf UniSZA Private Access Performance of IF-Postdiffset and R-Eclat Variants in Large Dataset International Journal of Engineering & Technology Pattern mining refers to a subfield of data mining that uncovers interesting, unexpected, and useful patterns from transaction databases. Such patterns reflect frequent and infrequent patterns. An abundant literature has dedicated in frequent pattern mining and tremendous efficient algorithms for frequent itemset mining in the transaction database. Nonetheless, the infrequent pattern mining has emerged to be an interesting issue in discovering patterns that rarely occur in the transaction database. More researchers reckon that rare pattern occur-rences may offer valuable information in knowledge data discovery process. The R-Eclat is a novel algorithm that determines infrequent patterns in the transaction database. The multiple variants in the R-Eclat algorithm generate varied performances in infrequent mining patterns. This paper proposes IF-Postdiffset as a new variant in R-Eclat algorithm. This paper also highlights the performance of infrequent mining pattern from the transaction database among different variants of the R-Eclat algorithm regarding its execution time. 7 4.1 134-137
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| spellingShingle |
Performance of IF-Postdiffset and R-Eclat Variants in Large Dataset
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to all authors
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| summary |
Pattern mining refers to a subfield of data mining that uncovers interesting, unexpected, and useful patterns from transaction databases. Such patterns reflect frequent and infrequent patterns. An abundant literature has dedicated in frequent pattern mining and tremendous efficient algorithms for frequent itemset mining in the transaction database. Nonetheless, the infrequent pattern mining has emerged to be an interesting issue in discovering patterns that rarely occur in the transaction database. More researchers reckon that rare pattern occur-rences may offer valuable information in knowledge data discovery process. The R-Eclat is a novel algorithm that determines infrequent patterns in the transaction database. The multiple variants in the R-Eclat algorithm generate varied performances in infrequent mining patterns. This paper proposes IF-Postdiffset as a new variant in R-Eclat algorithm. This paper also highlights the performance of infrequent mining pattern from the transaction database among different variants of the R-Eclat algorithm regarding its execution time.
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| title |
Performance of IF-Postdiffset and R-Eclat Variants in Large Dataset
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| title_full |
Performance of IF-Postdiffset and R-Eclat Variants in Large Dataset
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| title_fullStr |
Performance of IF-Postdiffset and R-Eclat Variants in Large Dataset
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| title_full_unstemmed |
Performance of IF-Postdiffset and R-Eclat Variants in Large Dataset
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| title_short |
Performance of IF-Postdiffset and R-Eclat Variants in Large Dataset
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| title_sort |
performance of if-postdiffset and r-eclat variants in large dataset
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