A systematic approach to the assessment of fuzzy association rules
Data Mining and Knowledge Discovery
GRAANK: Exploiting Rank Correlations for Extracting Gradual Itemsets
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Similarity relations and fuzzy orderings
Information Sciences: an International Journal
PGLCM: Efficient Parallel Mining of Closed Frequent Gradual Itemsets
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Numerical representation of product transitive complete fuzzy orderings
Mathematical and Computer Modelling: An International Journal
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In mining gradual patterns the idea is to express co-variations of attributes, taking the direction of change of attribute values into account. These patterns are such as { the more A, the more B}, { the more A, the more B, the less C } or { the higher the speed, the higher the danger }. These patterns are denoted as { A≥B≥ }, { A≥B≥C≤ } or { speed≥danger≥ } respectively. Such patterns hold if the variation constraints simultaneously hold on the attributes. However, it is often hardly possible to compare attribute values, either because the values are taken from noisy data, or because it is difficult to consider that a small difference between two values is meaningful. In this context, we focus on the use of fuzzy orderings to take this into account. abstract environment.