Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining patterns in long sequential data with noise
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Efficient Mining of Intertransaction Association Rules
IEEE Transactions on Knowledge and Data Engineering
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
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 Weak Periodicities in Large Time Series
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
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
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficient mining of frequent episodes from complex sequences
Information Systems
Mining fuzzy periodic association rules
Data & Knowledge Engineering
A new data structure for asynchronous periodic pattern mining
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
TOD: Temporal outlier detection by using quasi-functional temporal dependencies
Data & Knowledge Engineering
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports
Journal of Systems and Software
Efficient mining strategy for frequent serial episodes in temporal database
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
Periodic pattern analysis of non-uniformly sampled stock market data
Intelligent Data Analysis
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Mining periodic patterns in time series databases is an important data mining problem with many applications. Previous studies have considered synchronous periodic patterns where misaligned occurrences are not allowed. However, asynchronous periodic pattern mining has received less attention and only been discussed for a sequence of symbols where each time point contains one event. In this paper, we propose a more general model of asynchronous periodic patterns from a sequence of symbol sets where a time slot can contain multiple events. Three parameters min\_rep, max\_dis, and global\_rep are employed to specify the minimum number of repetitions required for a valid segment of nondisrupted pattern occurrences, the maximum allowed disturbance between two successive valid segments, and the total repetitions required for a valid sequence. A 4-phase algorithm is devised to discover periodic patterns from a time series database presented in vertical format. The experiments demonstrate good performance and scalability with large frequent patterns.