Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
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
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
Mining Regular Patterns in Transactional Databases
IEICE - Transactions on Information and Systems
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
An efficient algorithm for frequent itemset mining on data streams
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Efficient mining top-k regular-frequent itemset using compressed tidsets
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Efficient frequent pattern mining based on Linear Prefix tree
Knowledge-Based Systems
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Discovering interesting patterns from high-speed data streams is a challenging problem in data mining. Recently, the support metric-based frequent pattern mining from data stream has achieved a great attention. However, the occurrence frequency of a pattern may not be an appropriate criterion for discovering meaningful patterns. Temporal regularity in occurrence behavior can be a key criterion for assessing the importance of patterns in several online applications such as market basket analysis, gene data analysis, network monitoring, and stock market. A pattern can be said regular if its occurrence behavior satisfies a user-given interval in the data steam. Mining regular patterns from static databases has recently been addressed. However, even though mining regular patterns from stream data is extremely required in online applications, no such algorithm has been proposed yet. Therefore, in this paper we develop a novel tree structure called Regular Pattern Stream tree (RPS-tree), and an efficient mining technique for discovering regular patterns over data stream. Using a sliding window method the RPS-tree captures the stream content, and with an efficient tree updating mechanism it constantly processes exact stream data when the stream flows. Extensive experimental analyses show that our RPS-tree is highly efficient in discovering regular patterns from a high-speed data stream.