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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50891

Authors: Salah Alghyaline
Jun-Wei Hsieh
Jim Z. C. Lai
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: data mining
frequent pattern
frequent itemsets
Date: 2016-05
Issue Date: 2018-10-29T07:41:33Z
Publisher: Journal of Marine Science and Technology
Abstract: Abstract: Discovering frequent itemsets is an essential task in association
rules mining and it is considered to be computationally
expensive. To find the frequent itemsets, the algorithm of frequent
pattern growth (FP-growth) is one of the best algorithms
for mining frequent patterns. However, many experimental
results have shown that building conditional FP-trees during
mining data using this FP-growth method will consume
most of CPU time. In addition, it requires a lot of space to save
the FP-trees. This paper presents a new approach for mining
frequent item sets from a transactional database without building
the conditional FP-trees. Thus, lots of computing time and
memory space can be saved. Experimental results indicate
that our method can reduce lots of running time and memory
usage based on the datasets obtained from the FIMI repository
Relation: 24(2) pp.184-191
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50891
Appears in Collections:[資訊工程學系] 期刊論文

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