Gradual inference rules in approximate reasoning
Information Sciences: an International Journal
Association Rules for Expressing Gradual Dependencies
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Fast extraction of gradual association rules: a heuristic based method
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Evolution patterns and gradual trends
International Journal of Intelligent Systems
Mining Frequent Gradual Itemsets from Large Databases
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Mining closed gradual patterns
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
PGP-mc: towards a multicore parallel approach for mining gradual patterns
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Fuzzy orderings for fuzzy gradual patterns
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
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Gradual dependencies of the form the more A, the more B offer valuable information that linguistically express relationships between variations of the attributes. Several formalisations and automatic extraction algorithms have been proposed recently. In this paper, we first present an overview of these methods. We then propose an algorithm that combines the principles of several existing approaches and benefits from efficient computational properties to extract frequent gradual itemsets.