ICDE '98 Proceedings of the Fourteenth 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
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Discovering Calendar-Based Temporal Association Rules
TIME '01 Proceedings of the Eighth International Symposium on Temporal Representation and Reasoning (TIME'01)
A parallel algorithm for mining multiple partial periodic patterns
Information Sciences: an International Journal
Projection-based partial periodic pattern mining for event sequences
Expert Systems with Applications: An International Journal
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Partial periodic patterns mining is a very interesting domain in data mining problem. In the previous studies, full and partial multiple periodic patterns mining problems are considered. The proposed methods, however, may produce redundant information and are inefficient. In this paper, a novel concept and new parameters are proposed to improve the performance of partial multiple periodic patterns mining. Instead of considering the whole database, the information needed for mining partial periodic patterns is transformed into a bit vector which can be stored in a main memory. A set of simulations is also performed to show the benefit of our approach.