Fuzzy Sets and Systems
Data structures and algorithm analysis in C (2nd ed.)
Data structures and algorithm analysis in C (2nd ed.)
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
TreeFinder: a First Step towards XML Data Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Indexing and Mining Free Trees
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Chopper: efficient algorithm for tree mining
Journal of Computer Science and Technology
From Crispness to Fuzziness: Three Algorithms for Soft Sequential Pattern Mining
IEEE Transactions on Fuzzy Systems
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Intuitionistic fuzzy XML query matching
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
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Mining frequent patterns from huge databases have been addressed for many years and results have been applied to many fields, including banking, marketing, biology, health, etc. Fuzzy approaches have been proposed in order to soften the constraints on the patterns found by the algorithms. However, when dealing with complex databases such as tree databases (as it is for instance the case for XML databases), only a few methods have been proposed in order to handle soft constraints in discovering the frequent subtrees from a forest of trees. Such algorithms can hardly deal with real data in a soft manner. Indeed, they consider a subtree as fully included in the super-tree, meaning that all the nodes must appear. In this paper, we extend this definition to fuzzy inclusion based on the idea that a tree is included to a certain degree within another one. This fuzzy degree being correlated to the number of matching nodes. We propose the FTMnodes method together with the associated definitions, and we report the experiments lead on synthetical and real databases, showing the interest of our approach.