Mining relevant interval rules
arXiv:1709.03267v15 citations
Originality Synthesis-oriented
AI Analysis
This work addresses the challenge of rule mining in numerical data for data analysis applications, but it appears incremental as it builds directly on prior methods.
The authors tackled the problem of mining relevant rules for numerical attributes by extending an existing method to extract interval-based pattern rules, and they implemented and evaluated their algorithm on real datasets.
This article extends the method of Garriga et al. for mining relevant rules to numerical attributes by extracting interval-based pattern rules. We propose an algorithm that extracts such rules from numerical datasets using the interval-pattern approach from Kaytoue et al. This algorithm has been implemented and evaluated on real datasets.