Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Incremental and interactive sequence mining
Proceedings of the eighth international conference on Information and knowledge management
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Depth first generation of long patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Principles of data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining a stream of transactions for customer patterns
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Sliding-window filtering: an efficient algorithm for incremental mining
Proceedings of the tenth international conference on Information and knowledge management
Maintaining stream statistics over sliding windows: (extended abstract)
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Querying and mining data streams: you only get one look a tutorial
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
RHist: adaptive summarization over continuous data streams
Proceedings of the eleventh international conference on Information and knowledge management
ICDE '95 Proceedings of the Eleventh 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
Finding Frequent Items in Data Streams
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Approximating a Data Stream for Querying and Estimation: Algorithms and Performance Evaluation
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
estWin: Online data stream mining of recent frequent itemsets by sliding window method
Journal of Information Science
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Mining sequential patterns from data streams: a centroid approach
Journal of Intelligent Information Systems
Efficient strategies for tough aggregate constraint-based sequential pattern mining
Information Sciences: an International Journal
Approximate mining of maximal frequent itemsets in data streams with different window models
Expert Systems with Applications: An International Journal
GraSeq: A Novel Approximate Mining Approach of Sequential Patterns over Data Stream
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
A practical extension of web usage mining with intentional browsing data toward usage
Expert Systems with Applications: An International Journal
A new sampling technique for association rule mining
Journal of Information Science
A model updating strategy for predicting time series with seasonal patterns
Applied Soft Computing
Mining sequential patterns in the B2B environment
Journal of Information Science
Incremental mining of closed inter-transaction itemsets over data stream sliding windows
Journal of Information Science
MHUI-max: An efficient algorithm for discovering high-utility itemsets from data streams
Journal of Information Science
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With the usefulness of data mining in various fields of information science, various mining methods have been proposed in previous research. Recently, in these fields, data has taken the form of continuous data streams rather than finite stored data sets. In this paper, a mining method of sequential patterns over an online sequence data stream is proposed, which is useful for retrieving embedded knowledge in the data stream. The proposed method can minimize memory usage of the mining process while an error is allowed in its mining result, and supports flexible trade-off between memory usage and mining accuracy. However, the error is minimized by an accurate estimation method for the count of a sequence, which considers the ordering information of items. The proposed method can catch a recent change in a sequence data stream in a short time, by a decaying mechanism gracefully discarding old information that may be no longer useful.