Interestingness measures for association rules based on statistical validity
Assessing rules with interestingness measures is the pillar of successful application of association rules discovery.However, association rules discovered are normally large in number, some of which are not considered as interesting or significant for the application at hand. In this paper, we pres...
Saved in:
| Main Authors: | , , |
|---|---|
| 格式: | Article |
| 语言: | English |
| 出版: |
Elsevier B.V
2011
|
| 主题: | |
| 在线阅读: | http://repo.uum.edu.my/12544/1/WP07.pdf http://repo.uum.edu.my/12544/ http://dx.doi.org/10.1016/j.knosys.2010.11.005 |
| 标签: |
添加标签
没有标签, 成为第一个标记此记录!
|
| 总结: | Assessing rules with interestingness measures is the pillar of successful application of association rules discovery.However, association rules discovered are normally large in number, some of which are not
considered as interesting or significant for the application at hand. In this paper, we present a systematic approach to ascertain the discovered rules, and provide a precise statistical approach supporting this
framework.The proposed strategy combines data mining and statistical measurement techniques,
including redundancy analysis, sampling and multivariate statistical analysis, to discard the non significant rules.Moreover, we consider real world datasets which are characterized by the uniform and non-
uniform data/items distribution with a mixture of measurement levels throughout the data/items.The proposed unified framework is applied on these datasets to demonstrate its effectiveness in discarding many of the redundant or non-significant rules, while still preserving the high accuracy of the rule set as a whole. |
|---|