A mass assignment theory of the probability of fuzzy events
Fuzzy Sets and Systems
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Possibility Theory, Probability Theory and Multiple-Valued Logics: A Clarification
Annals of Mathematics and Artificial Intelligence
A probabilistic definition of a nonconvex fuzzy cardinality
Fuzzy Sets and Systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Granular computing using information tables
Data mining, rough sets and granular computing
Intensional theory of granular computing
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A systematic approach to the assessment of fuzzy association rules
Data Mining and Knowledge Discovery
Interoperability among Distributed Overlapping Ontologies--A Fuzzy Ontology Framework
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Fuzzy sets in machine learning and data mining
Applied Soft Computing
Improving access to multimedia using multi-source hierarchical meta-data
AMR'05 Proceedings of the Third international conference on Adaptive Multimedia Retrieval: user, context, and feedback
Fuzzy association rules: general model and applications
IEEE Transactions on Fuzzy Systems
Incremental Evolution of Fuzzy Grammar Fragments to Enhance Instance Matching and Text Mining
IEEE Transactions on Fuzzy Systems
| Hi-index | 0.00 |
The use of hierarchical taxonomies to organise information (or sets of objects) is a common approach for the semantic web and elsewhere, and is based on progressively finer granulations of objects. In many cases, seemingly crisp granulation disguises the fact that categories are based on loosely defined concepts that are better modelled by allowing graded membership. A related problem arises when different taxonomies are used, with different structures, as the integration process may also lead to fuzzy categories. Care is needed when information systems use fuzzy sets to model graded membership in categories - the fuzzy sets are not disjunctive possibility distributions, but must be interpreted conjunctively. We clarify this distinction and show how an extended mass assignment framework can be used to extract relations between fuzzy categories. These relations are association rules and are useful when integrating multiple information sources categorised according to different hierarchies. Our association rules do not suffer from problems associated with use of fuzzy cardinalities. Experimental results on discovering association rules in film databases and terrorism incident databases are demonstrated.