Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Infominer: mining surprising periodic patterns
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Partially Periodic Event Patterns with Unknown Periods
Proceedings of the 17th International Conference on Data Engineering
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
Incremental, Online, and Merge Mining of Partial Periodic Patterns in Time-Series Databases
IEEE Transactions on Knowledge and Data Engineering
Discovering High-Order Periodic Patterns
Knowledge and Information Systems
SMCA: A General Model for Mining Asynchronous Periodic Patterns in Temporal Databases
IEEE Transactions on Knowledge and Data Engineering
Multi-level fuzzy mining with multiple minimum supports
Expert Systems with Applications: An International Journal
Discovery of Periodic Patterns in Spatiotemporal Sequences
IEEE Transactions on Knowledge and Data Engineering
Mining fuzzy periodic association rules
Data & Knowledge Engineering
Efficient Periodicity Mining in Time Series Databases Using Suffix Trees
IEEE Transactions on Knowledge and Data Engineering
GCIS '10 Proceedings of the 2010 Second WRI Global Congress on Intelligent Systems - Volume 02
Knowledge discovery of weighted RFM sequential patterns from customer sequence databases
Journal of Systems and Software
Constrained frequent pattern mining on univariate uncertain data
Journal of Systems and Software
An efficient tree-based algorithm for mining sequential patterns with multiple minimum supports
Journal of Systems and Software
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The problem of mining partial periodic patterns is an important issue with many applications. Previous studies to find these patterns encounter efficiency and effectiveness problem. The efficiency problem is that most previous methods were proposed to find frequent partial periodic patterns by extending the well-known Apriori-like algorithm. However, these methods generate many candidate partial periodic patterns to calculate the patterns' supports, spending much time for discovering patterns. The effective problem is that only one minimum support threshold is set to find frequent partial periodic patterns but the results is not practical for real-world. In real-life circumstances, some rare or specific events may occur with lower frequencies but their occurrences may offer some vital information to be referred in decision making. If the minimum support is set too high, the associations between events along with higher and lower frequencies cannot be evaluated so that significant knowledge will be ignored. In this study, an algorithm to overcome these two problems has been proposed to generating redundant candidate patterns and setting only one minimum support threshold. The algorithm greatly improves the efficiency and effectiveness. First, it eliminates the need to generate numerous candidate partial periodic patterns thus reducing database scanning. Second, the minimum support threshold of each event can be specified based in its real-life occurring frequency.