An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 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
A localized algorithm for parallel association mining
Proceedings of the ninth annual ACM symposium on Parallel algorithms and architectures
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Cyclically Repeated Patterns
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
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)
Parallel Association Rule Mining with Minimum Inter-Processor Communication
DEXA '03 Proceedings of the 14th International Workshop on Database and Expert Systems Applications
Parallel Mining of Maximal Frequent Itemsets from Databases
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Incremental, Online, and Merge Mining of Partial Periodic Patterns in Time-Series Databases
IEEE Transactions on Knowledge and Data Engineering
Efficient Partial Multiple Periodic Patterns Mining without Redundant Rules
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
Periodicity Detection in Time Series Databases
IEEE Transactions on Knowledge and Data Engineering
An efficient algorithm for mining frequent inter-transaction patterns
Information Sciences: an International Journal
Finding calendar-based periodic patterns
Pattern Recognition Letters
An association-based case reduction technique for case-based reasoning
Information Sciences: an International Journal
RP-Tree: A Tree Structure to Discover Regular Patterns in Transactional Database
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Mining Regular Patterns in Transactional Databases
IEICE - Transactions on Information and Systems
Mining Calendar-Based Periodicities of Patterns in Temporal Data
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
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The mining of partial periodic patterns is an interesting type of data mining that is widely used in the analysis of markets, such as for stock management and sales management. However, the existence of huge data sets make the scalability of data-mining algorithms a very important objective, and in recent years parallel computing has been applied to general data-mining algorithms. This paper addresses the problem of mining multiple partial periodic patterns in a parallel computing environment. To reduce the cost of communication between the processors, our approach employs the independence property of prime numbers to classify partial periodic patterns into multiple independent sets. Moreover, a novel method of distributing mining tasks among the processors is proposed. A set of simulations is used to demonstrate the benefits of our approach.