Mining High Utility Partial Periodic Pattern by GPA

被引：0

作者：

Hong, Tzung-Pei ^{[1
]}

Hsu, Jen-Hao ^{[2
]}

Yang, Kung-Jiuan ^{[3
]}

Lan, Guo-Cheng ^{[4
]}

Lin, Jerry Chun-Wei ^{[5
]}

Wang, Shyue-Liang ^{[6
]}

机构：

[1] Natl Univ Kaohsiung, Dept Comp Sci & Informat Engn, Kaohsiung, Taiwan

[2] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung, Taiwan

[3] Natl Cheng Kung Univ, Dept Mfg Informat & Syst, Tainan, Taiwan

[4] Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn, Tainan, Taiwan

[5] Harbin Inst Technol, Shenzhen Grad Sch, Sch Comp Sci & Technol, Shenzhen, Peoples R China

[6] Natl Univ Kaohsiung, Dept Informat Management, Kaohsiung, Taiwan

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2017年

关键词：

data mining; gradually pruning; utility; partial periodic pattern;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Different from full periodic patterns, partial periodic patterns could ignore the occurrence of some events in time positions. In this paper, we have presented a gradually pruning algorithm (GPA) for reducing the number of candidate patterns in the mining process. It is based on the two-phased periodic utility upper-bound (PUUB) model and could avoid information loss. Compared to the original approach without gradually pruning, the one proposed here could reduce the execution time but get the same desired results.

引用

页码：820 / 824

页数：5

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