A Sensitivity Analysis for Quality Measures of Quantitative Association Rules

被引：0
|
作者：
Martinez-Ballesteros, Mara [1 ]
Martinez-Alvarez, Francisco [2 ]
Troncoso, Alicia [2 ]
Riquelme, Jose C. [1 ]
机构：
[1] Univ Seville, Dept Comp Sci, Seville, Spain
[2] Pablo Olavide Univ Seville, Dept Comp Sci, Seville, Spain
来源：
HYBRID ARTIFICIAL INTELLIGENT SYSTEMS | 2013年 / 8073卷
关键词：
Data mining; sensitivity analysis; quantitative association rules; quality measures; ALGORITHM;
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：
There exist several fitness function proposals based on a combination of weighted objectives to optimize the discovery of association rules. Nevertheless, some differences in the measures used to assess the quality of association rules could be obtained according to the values of such weights. Therefore, in such proposals it is very important the user's decision in order to specify the weights or coefficients of the optimized objectives. Thus, this work presents an analysis on the sensitivity of several quality measures when the weights included in the fitness function of the existing QARGA algorithm are modified. Finally, a comparative analysis of the results obtained according to the weights setup is provided.
引用
收藏
页码：578 / 587
页数：10
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