Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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
Small is beautiful: discovering the minimal set of unexpected patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
The PSP Approach for Mining Sequential Patterns
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Managing Interesting Rules in Sequence Mining
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering all most specific sentences
ACM Transactions on Database Systems (TODS)
A novel method for discovering fuzzy sequential patterns using the simple fuzzy partition method
Journal of the American Society for Information Science and Technology
A fuzzy data mining algorithm for finding sequential patterns
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Discovery of Fuzzy Sequential Patterns for Fuzzy Partitions in Quantitative Attributes
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
Fuzzy data mining for interesting generalized association rules
Fuzzy Sets and Systems - Theme: Learning and modeling
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Computational complexity of itemset frequency satisfiability
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
On Characterization and Discovery of Minimal Unexpected Patterns in Rule Discovery
IEEE Transactions on Knowledge and Data Engineering
Mining association rules from imprecise ordinal data
Fuzzy Sets and Systems
A new approach for discovering fuzzy quantitative sequential patterns in sequence databases
Fuzzy Sets and Systems
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The recognition of unexpected behaviors in databases is an important problem in many real-world applications. In the previous studies, the unexpectedness is mainly stated within the context of the most-studied patterns, association rules, or sequential patterns. In this paper, we first propose the notion of fuzzy recurrence rule, a new kind of rule-based behavior in sequence databases, and then we introduce the problem of recognizing unexpected sequences contradicting the beliefs on fuzzy recurrence rules, with fuzzy measures. We also develop, UFR, an algorithm for discovering unexpected recurrence behaviors in a sequence database. Our approach is evaluated with Web access log data.