Computing exact permutation p-values for association rules
| Autor: | Jianyu Zhou, Zengyou He, Xiaoqing Liu, Can Yang, Jun Wu, Feiyang Gu |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Information Systems and Management
Association rule learning business.industry Context (language use) Usability 02 engineering and technology Random permutation computer.software_genre Data type Field (computer science) Computer Science Applications Theoretical Computer Science Task (project management) Permutation Artificial Intelligence Control and Systems Engineering 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining business computer Software Mathematics |
| Zdroj: | Information Sciences. :146-162 |
| ISSN: | 0020-0255 |
| DOI: | 10.1016/j.ins.2016.01.094 |
| Popis: | Association rule mining is an important task in the field of data mining, and many efficient algorithms have been proposed to address this problem. However, a large portion of the rules reported by these algorithms just satisfy the user-defined constraints purely by accident, and those that are not statistically meaningful should be filtered out through statistical significance testing. In the context of association rule discovery, the permutation-based approach can achieve better performance than other competitive methods, although several drawbacks of this effective approach narrow its usability. In this paper, we provide an analysis of these disadvantages and propose an algorithm called Exact Permutation p-values for Association Rules (EPAR) to calculate the exact p-values of all tested rules. Experiments on different types of data sets demonstrate that EPAR can successfully alleviate the disadvantages and outperform the direct permutation-based method over several performance measures. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect