An Informative Base of Positive and Negative Association Rules on Big Data
| Autor: | Totohasina André, Bemarisika Parfait |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Association rule learning
Computer science business.industry Big data Context (language use) 02 engineering and technology Negative association Base (topology) Machine learning computer.software_genre 03 medical and health sciences 0302 clinical medicine Order (exchange) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 030212 general & internal medicine Artificial intelligence business computer |
| Zdroj: | IEEE BigData |
| DOI: | 10.1109/bigdata47090.2019.9005955 |
| Popis: | The concept of informative base for association rules is the subject of many approaches. However, these approaches are based on positive rules but not on negative rules, and this with the less selective support-confidence pair. So that, these positive rules are not enough to cover all needs in context of Big Data, it also needs the negative association rules. In order to overcome these limitations, we propose a new approach for positive and negative association rules using the new selective pair, support -M GK . We also introduce NONREDRULES algorithm for mining all informative association rules. The experimental evaluation on the reference databases presents the extensive feasibility of our approach on the context of Big Data. |
| Databáze: | OpenAIRE |
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