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Weighted association rules mining algorithm research

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成果类型:
期刊论文、会议论文
作者:
Tan, Jun*
通讯作者:
Tan, Jun
作者机构:
[Tan, Jun; Tan, J] Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha, Hunan, Peoples R China.
通讯机构:
[Tan, Jun] C
Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha, Hunan, Peoples R China.
语种:
英文
关键词:
anti-monotonicity;weighted association rules;weighted support;Apriori algorithm
期刊:
Applied Mechanics and Materials
ISSN:
1660-9336
年:
2013
卷:
241-244
页码:
1598-1601
会议名称:
International Conference on Measurement, Instrumentation and Automation (ICMIA 2012)
会议论文集名称:
Applied Mechanics and Materials
会议时间:
SEP 15-16, 2012
会议地点:
Guangzhou, PEOPLES R CHINA
会议主办单位:
Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha, Hunan, Peoples R China.
会议赞助商:
Queensland Univ Technol, Korea Maritime Univ, Inha Univ
主编:
Yarlagadda, P Kim, YH
出版地:
KREUZSTRASSE 10, 8635 DURNTEN-ZURICH, SWITZERLAND
出版者:
TRANS TECH PUBLICATIONS LTD
ISBN:
978-3-03785-546-1
机构署名:
本校为第一且通讯机构
院系归属:
计算机与信息工程学院
摘要:
Aiming at the problem that most of weighted association rules mining algorithms have not the anti-monotonicity, this paper presents a weighted support-confidence framework which supports anti-monotonicity. On this basis, weighted boolean association rules mining algorithm and weighted fuzzy association rules mining algorithm are presented, which use pruning strategy of Apriori algorithm so that improve the efficiency of frequent itemsets generated. Experimen...

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