Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Data Mining
Fuzzy data mining based on the compressed fuzzy FP-trees
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Linguistic data mining with fuzzy FP-trees
Expert Systems with Applications: An International Journal
Integration of multiple fuzzy FP-trees
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
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A significant data mining issue is the effective discovery of association rules. The extraction of association rules faces the problem of the combinatorial explosion of the search space, and the loss of information by the discretization of values. The first problem is confronted effectively by the Frequent Pattern Tree approach of [10]. This approach avoids the candidate generation phase of Apriori like algorithms. But, the discretization of the values of the attributes (i.e. the "items") at the basic Frequent Pattern Tree approach implies a loss of information. This loss usually either deteriorates significantly the results, or constitues them completely intolerable. This work extends appropriately the Frequent Pattern Tree approach in the fuzzy domain. The presented Fuzzy Frequent Pattern Tree retains the efficiency of the crisp Frequent Pattern Tree, while at the same time the careful updating of the fuzzy sets at all the phases of the algorithm tries to preserve most of the original information at the data set. The paper presents an application of the Fuzzy Frequent Pattern Tree approach to the difficult problem of the discovery of fuzzy association rules between genes from massive gene expression measurements.