.png){kind=icon}
.png){kind=icon}
Return
Mining frequent patterns and association rules using similarities
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2013.06.041.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Data mining
Frequent patterns
Association rules
Mixed data
Similarity functions
Downward closure property
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W