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
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
GRAANK: Exploiting Rank Correlations for Extracting Gradual Itemsets
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Debugging embedded multimedia application traces through periodic pattern mining
Proceedings of the tenth ACM international conference on Embedded software
Para Miner: a generic pattern mining algorithm for multi-core architectures
Data Mining and Knowledge Discovery
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Mining gradual rules of the form - "the more A, the more B"- is more and more grasping the interest of the data mining community. Several approaches have been recently proposed. Unfortunately, in all surveyed approaches, reducing the quantity of mined patterns (and, consequently, the quantity of extracted rules) was not the main concern. To palliate such a drawback, a possible solution consists in using results of Formal Concept Analysis to generate a lossless reduced size nucleus of gradual patterns. To do so, we introduce in this paper a novel closure operator acting on gradual itemsets. Results of the experiments carried out on synthetic datasets showed important profits in terms of compactness of the generated gradual patterns set.