GA2RM: A GA-Based Action Rule Mining Method
| Autor: | Shervin Hashemi, Pirooz Shamsinejad |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Action rule 02 engineering and technology computer.software_genre Computer Science Applications Theoretical Computer Science Action (philosophy) Decision system 020204 information systems Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining computer Software |
| Zdroj: | International Journal of Computational Intelligence and Applications. 20 |
| ISSN: | 1757-5885 1469-0268 |
| DOI: | 10.1142/s1469026821500127 |
| Popis: | Action Mining is a subfield of Data Mining that tries to extract actions from traditional data sets. Action Rule is a type of rule that suggests some changes in its consequent part. Extracting action rules from data has been one of the research interests in recent years. Current state-of-the-art action rule mining methods like DEAR typically take classification rules as their input; Since traditional classification methods have been designed for prediction and not for manipulation, therefore extracting action rules directly from data can result in more valuable action rules. Here, we have proposed a method to generate action rules directly from data. To tackle the problem of huge search space of action rules, a Genetic Algorithm has been devised. Different metrics have been defined for investigating the effectiveness of our proposed method and a large number of experiments have been done on real and synthetic data sets. The results show that our method can find from 20% to 10 times more interesting (in case of support and confidence) action rules in comparison with its competitors. |
| Databáze: | OpenAIRE |
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