Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Mining fuzzy association rules in databases
ACM SIGMOD Record
ACM Transactions on Information Systems (TOIS)
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
A New Approach to Online Generation of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of Intertransaction Association Rules
IEEE Transactions on Knowledge and Data Engineering
Efficient Automated Mining of Fuzzy Association Rules
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Mining Fuzzy Quantitative Association Rules
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Fuzzy association rules: general model and applications
IEEE Transactions on Fuzzy Systems
Online mining of fuzzy multidimensional weighted association rules
Applied Intelligence
Mining generalized association rules with quantitative data under multiple support constraints
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
MOGA-based fuzzy data mining with taxonomy
Knowledge-Based Systems
| Hi-index | 0.00 |
This paper addresses fuzzy weighted multi-cross-level association rule mining. We define a fuzzy data cube, which facilitates for handling quantitative values of dimensional attributes, and hence allows for mining fuzzy association rules at different levels. A method is introduced for single dimension fuzzy weighted association rules mining. To the best of our knowledge, none of the studies described in the literature considers weighting the internal nodes in such taxonomy. Only items appearing in transactions are weighted to find more specific and important knowledge. But, sometimes weighting internal nodes on a tree may be more meaningful and enough. We compared the proposed approach to an existing approach that does not utilize fuzziness. The reported experimental results demonstrate the effectiveness and applicability of the proposed fuzzy weighted multi-cross-level mining approach.