Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A logic for reasoning with inconsistency
Journal of Automated Reasoning
Integration of weighted knowledge bases
Artificial Intelligence
BIG: an agent for resource-bounded information gathering and decision making
Artificial Intelligence - Special issue on Intelligent internet systems
ACM SIGKDD Explorations Newsletter
Possibilistic Merging and Distance-Based Fusion of Propositional Information
Annals of Mathematics and Artificial Intelligence
Arbitration (or How to Merge Knowledge Bases)
IEEE Transactions on Knowledge and Data Engineering
Identifying Relevant Databases for Multidatabase Mining
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Measuring inconsistency in knowledge via quasi-classical models
Eighteenth national conference on Artificial intelligence
Quasi-possibilistic logic and its measures of information and conflict
Fundamenta Informaticae
Mining Multiple Data Sources: Local Pattern Analysis
Data Mining and Knowledge Discovery
IT support for healthcare processes - premises, challenges, perspectives
Data & Knowledge Engineering
Data & Knowledge Engineering
Analysis of Naive Bayes' assumptions on software fault data: An empirical study
Data & Knowledge Engineering
Making argumentation more believable
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Merging stratified knowledge bases under constraints
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Uncertainty, belief, and probability
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Evaluating significance of inconsistencies
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Measuring conflict and agreement between two prioritized belief bases
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Mining frequent patterns from univariate uncertain data
Data & Knowledge Engineering
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This paper presents a formal framework for multiple data source (MDS) discovery. A measure is first proposed for estimating the consistency, inconsistency and uncertainty between data sources using possibilistic minimal model. Then, two metrics are defined for measuring the support and confidence of a set of formulae (itemsets) in terms of the degree of consistency of the items. The consistency measure, in conjunction with support-confidence framework in data mining, assists in identifying interesting knowledge from MDSs. Finally, the impact of consistency among knowledge bases is considered to determine the knowledge base from which a set of formulae is most likely identified as a pattern of interest. A major advantage of this framework is that the mining algorithm supports the reasoning about the knowledge from possibilistic data-sources. We evaluate the proposed approach with both examples and experiment, and demonstrate that our method is useful and efficient in identifying interesting patterns from multiple databases.