Fuzzy sets and vague environments
Fuzzy Sets and Systems - Special issue on diagnostics and control through neural interpretations of fuzzy sets
Mining fuzzy association rules in databases
ACM SIGMOD Record
Temporal Granularity: Completing the Puzzle
Journal of Intelligent Information Systems
KDD-Cup 2000 organizers' report: peeling the onion
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Infominer: mining surprising periodic patterns
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A general framework for time granularity and its application to temporal reasoning
Annals of Mathematics and Artificial Intelligence
An Algebraic Representation of Calendars
Annals of Mathematics and Artificial Intelligence
Efficiently Supporting Temporal Granularities
IEEE Transactions on Knowledge and Data Engineering
Discovering calendar-based temporal association rules
Data & Knowledge Engineering - Special issue: Temporal representation and reasoning
Symbolic User-Defined Periodicity in Temporal Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Implementing Calendars and Temporal Rules in Next Generation Databases
Proceedings of the Tenth International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Mining Partially Periodic Event Patterns with Unknown Periods
Proceedings of the 17th International Conference on Data Engineering
On the Discovery of Interesting Patterns in Association Rules
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Asynchronous Periodic Patterns in Time Series Data
IEEE Transactions on Knowledge and Data Engineering
A Paradigm for Detecting Cycles in Large Data Sets via Fuzzy Mining
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
A Model for Time Granularity in Natural Language
TIME '98 Proceedings of the Fifth International Workshop on Temporal Representation and Reasoning
A Logical Approach to Represent and Reason about Calendars
TIME '02 Proceedings of the Ninth International Symposium on Temporal Representation and Reasoning (TIME'02)
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Introducing Uncertainty into Pattern Discovery in Temporal Event Sequences
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Incremental, Online, and Merge Mining of Partial Periodic Patterns in Time-Series Databases
IEEE Transactions on Knowledge and Data Engineering
SMCA: A General Model for Mining Asynchronous Periodic Patterns in Temporal Databases
IEEE Transactions on Knowledge and Data Engineering
Discovery of fuzzy temporal association rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mining dynamic association rules with comments
Knowledge and Information Systems
New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports
Journal of Systems and Software
| Hi-index | 0.00 |
We develop techniques for discovering patterns with periodicity in this work. Patterns with periodicity are those that occur at regular time intervals, and therefore there are two aspects to the problem: finding the pattern, and determining the periodicity. The difficulty of the task lies in the problem of discovering these regular time intervals, i.e., the periodicity. Periodicities in the database are usually not very precise and have disturbances, and might occur at time intervals in multiple time granularities. To overcome these difficulties and to be able to discover the patterns with fuzzy periodicity, we propose the fuzzy periodic calendar which defines fuzzy periodicities. Furthermore, we develop algorithms for mining fuzzy periodicities and the fuzzy periodic association rules within them. Experimental results have shown that our method is effective in discovering fuzzy periodic association rules.