Name
Abstract | ||
|---|---|---|
We propose a general framework to formalize the problem of capturing the intensity of implication for association rules through statistical metrics. In this framework we present properties that influence the interestingness of a rule, analyze the conditions that lead a measure to perform a perfect prune at a time, and define a final proper order to sort the surviving rules. We will discuss why none of the currently employed measures can capture objective interestingness, and just the combination of some of them in a multi-step fashion, can be reliable. In contrast, we propose a new simple modification of the Pearson coefficient that will meet all the necessary requirements. We statistically infer the convenient cut-off threshold for this new metric by empirically describing its distribution function through simulation. Experiments show a promising behaviour of our proposal. |
Year | Venue | Keywords |
|---|---|---|
2004 | FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS | association rule |
Field | DocType | Volume |
Data mining,Pearson product-moment correlation coefficient,Computer science,sort,Association rule learning,Artificial intelligence,Machine learning,Pruning | Conference | 110 |
ISSN | Citations | PageRank |
0922-6389 | 4 | 0.40 |
References | Authors | |
12 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gemma Casas-garriga | 1 | 46 | 3.79 |