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 Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Pre-Processing Time Constraints for Efficiently Mining Generalized Sequential Patterns
TIME '04 Proceedings of the 11th International Symposium on Temporal Representation and Reasoning
Gradual elements in a fuzzy set
Soft Computing - A Fusion of Foundations, Methodologies and Applications
GRAANK: Exploiting Rank Correlations for Extracting Gradual Itemsets
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Speed up gradual rule mining from stream data! A B-Tree and OWA-based approach
Journal of Intelligent Information Systems
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
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Even if they have proven to be relevant on traditional transactional databases, data mining tools are still inefficient on some kinds of databases. In particular, databases containing discrete values or having a value for each item, like gene expression data, are especially challenging. On such data, existing approaches either transform the data to classical binary attributes, or use discretisation, including fuzzy partition to deal with the data. However, binary mapping of such databases drives to a loss of information and extracted knowledge is not exploitable for end-users. Thus, powerful tools designed for this kind of data are needed. On the other hand, existing fuzzy approaches hardly take gradual notions into account, or are not scalable enougth to tackle the problem. In this paper, we thus propose a heuristic in order to extract tendencies, in the form of gradual association rules. A gradual rule can be read as "The more X and the less Y, then the more V and the less W". Instead of using fuzzy sets, we apply our method directly on valued data and we propose an efficient heuristic, thus reducing combinatorial complexity and scalability. Experiments on synthetic datasets show the interest of our method.