On the representation and querying of sets of possible worlds
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
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 probabilistic relational model and algebra
ACM Transactions on Database Systems (TODS)
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Mining fuzzy association rules in databases
ACM SIGMOD Record
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Fuzzy association rules and the extended mining algorithms
Information Sciences—Informatics and Computer Science: An International Journal
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
A Fuzzy Approach for Mining Quantitative Association Rules
A Fuzzy Approach for Mining Quantitative Association Rules
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Mining association rules with non-uniform privacy concerns
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Algorithms for mining association rules in bag databases
Information Sciences—Informatics and Computer Science: An International Journal
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A systematic approach to the assessment of fuzzy association rules
Data Mining and Knowledge Discovery
Sketching probabilistic data streams
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Efficient query evaluation on probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient Mining of Frequent Patterns from Uncertain Data
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Finding frequent items in probabilistic data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A Survey of Uncertain Data Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
On Unifying Privacy and Uncertain Data Models
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Frequent pattern mining with uncertain data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic frequent itemset mining in uncertain databases
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
MayBMS: a probabilistic database management system
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Representing uncertain data: models, properties, and algorithms
The VLDB Journal — The International Journal on Very Large Data Bases
Mining frequent itemsets from uncertain data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
A decremental approach for mining frequent itemsets from uncertain data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A tree-based approach for frequent pattern mining from uncertain data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mining uncertain data with probabilistic guarantees
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Accelerating probabilistic frequent itemset mining: a model-based approach
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Mining fuzzy association rules from uncertain data
Knowledge and Information Systems
Approximation of Frequentness Probability of Itemsets in Uncertain Data
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Continuous monitoring of skylines over uncertain data streams
Information Sciences: an International Journal
Models for incomplete and probabilistic information
EDBT'06 Proceedings of the 2006 international conference on Current Trends in Database Technology
Efficient pattern mining of uncertain data with sampling
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Generalized association rule mining with constraints
Information Sciences: an International Journal
Incremental update on probabilistic frequent itemsets in uncertain databases
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Fuzzy association rules: general model and applications
IEEE Transactions on Fuzzy Systems
On the representation, measurement, and discovery of fuzzy associations
IEEE Transactions on Fuzzy Systems
Fast tree-based mining of frequent itemsets from uncertain data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
A formal model for mining fuzzy rules using the RL representation theory
Information Sciences: an International Journal
Discovering Threshold-based Frequent Closed Itemsets over Probabilistic Data
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Learning very fast decision tree from uncertain data streams with positive and unlabeled samples
Information Sciences: an International Journal
UFIMT: an uncertain frequent itemset mining toolbox
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent itemsets over uncertain databases
Proceedings of the VLDB Endowment
Probabilistic frequent pattern growth for itemset mining in uncertain databases
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Efficient Mining of Frequent Item Sets on Large Uncertain Databases
IEEE Transactions on Knowledge and Data Engineering
Probabilistic top-k dominating queries in uncertain databases
Information Sciences: an International Journal
Software defect prediction using relational association rule mining
Information Sciences: an International Journal
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Current studies on association rule mining focus on finding Boolean/quantitative association rules from certain databases or Boolean association rules from probabilistic databases. However, little work on mining association rules from probabilistic quantitative databases has been mentioned because the simultaneous measurement of quantitative information and probability is difficult. By introducing a novel Shannon-like Entropy, we aggregate and measure the information contained in an item with the coexistence of fuzzy uncertainty hidden in quantitative values and random uncertainty. We then propose Support and Confidence metrics for a fuzzy-probabilistic database to quantify association rules. Finally, we design an algorithm, called FARP (mining Fuzzy Association Rules from a Probabilistic quantitative data), to discover frequent fuzzy-probabilistic itemsets and fuzzy association rules using the proposed interest measures. The experimental results show the effectiveness of our method and its practicality in real-world applications.