Mining frequent patterns and association rules using similarities

被引：23

作者：

Rodriguez-Gonzalez, Ansel Y. ^{[1
]}

Fco. Martinez-Trinidad, Jose ^{[1
]}

Carrasco-Ochoa, Jesus A. ^{[1
]}

Ruiz-Shulcloper, Jose ^{[2
]}

机构：

[1] Inst Astrophys Opt & Elect INAOE, Dept Comp Sci, Puebla 72840, Mexico

[2] Adv Technol Applicat Ctr CENATAV, Havana 12200, Cuba

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2013年 / 40卷 / 17期

关键词：

Data mining; Frequent patterns; Association rules; Mixed data; Similarity functions; Downward closure property; CLASSIFICATION; ALGORITHM; DISEASE;

D O I：

10.1016/j.eswa.2013.06.041

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Most of the current algorithms for mining association rules assume that two object subdescriptions are similar when they are exactly equal, but in many real world problems some other similarity functions are used. Commonly these algorithms are divided in two steps: Frequent pattern mining and generation of interesting association rules from frequent patterns. In this work, two algorithms for mining frequent similar patterns using similarity functions different from the equality are proposed. Additionally, the GenRules Algorithm is adapted to generate interesting association rules from frequent similar patterns. Experimental results show that our algorithms are more effective and obtain better quality patterns than the existing ones. (C) 2013 Elsevier Ltd. All rights reserved.

引用

页码：6823 / 6836

页数：14

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