Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining data streams under block evolution
ACM SIGKDD Explorations Newsletter
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Efficiently Mining Maximal Frequent Itemsets
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
SmartMiner: A Depth First Algorithm Guided by Tail Information for Mining Maximal Frequent Itemsets
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A framework for diagnosing changes in evolving data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
estWin: Online data stream mining of recent frequent itemsets by sliding window method
Journal of Information Science
Efficient mining method for retrieving sequential patterns over online data streams
Journal of Information Science
DSM-PLW: single-pass mining of path traversal patterns over streaming web click-sequences
Computer Networks: The International Journal of Computer and Telecommunications Networking - Web dynamics
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A regression-based temporal pattern mining scheme for data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Online mining maximal frequent structures in continuous landmark melody streams
Pattern Recognition Letters
Interactive mining of top-K frequent closed itemsets from data streams
Expert Systems with Applications: An International Journal
Experimental study on fighters behaviors mining
Expert Systems with Applications: An International Journal
Incremental mining of closed inter-transaction itemsets over data stream sliding windows
Journal of Information Science
Efficient prime-based method for interactive mining of frequent patterns
Expert Systems with Applications: An International Journal
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
Mining frequent patterns in a varying-size sliding window of online transactional data streams
Information Sciences: an International Journal
Mining maximal frequent patterns by considering weight conditions over data streams
Knowledge-Based Systems
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A data stream is a massive, open-ended sequence of data elements continuously generated at a rapid rate. Mining data streams is more difficult than mining static databases because the huge, high-speed and continuous characteristics of streaming data. In this paper, we propose a new one-pass algorithm called DSM-MFI (stands for Data Stream Mining for Maximal Frequent Itemsets), which mines the set of all maximal frequent itemsets in landmark windows over data streams. A new summary data structure called summary frequent itemset forest (abbreviated as SFI-forest) is developed for incremental maintaining the essential information about maximal frequent itemsets embedded in the stream so far. Theoretical analysis and experimental studies show that the proposed algorithm is efficient and scalable for mining the set of all maximal frequent itemsets over the entire history of the data streams.