Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and 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
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Fast vertical mining using diffsets
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
Catch the moment: maintaining closed frequent itemsets over a data stream sliding window
Knowledge and Information Systems
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Towards a new approach for mining frequent itemsets on data stream
Journal of Intelligent Information Systems
Data Mining and Knowledge Discovery
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Approximate mining of frequent patterns on streams
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Maintaining frequent closed itemsets over a sliding window
Journal of Intelligent Information Systems
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
Mining non-derivable frequent itemsets over data stream
Data & Knowledge Engineering
Verifying and Mining Frequent Patterns from Large Windows over Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Mining frequent itemsets in data streams using the weighted sliding window model
Expert Systems with Applications: An International Journal
estMax: Tracing Maximal Frequent Item Sets Instantly over Online Transactional Data Streams
IEEE Transactions on Knowledge and Data Engineering
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
Methods for finding frequent items in data streams
The VLDB Journal — The International Journal on Very Large Data Bases
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
A false negative approach to mining frequent itemsets from high speed transactional data streams
Information Sciences: an International Journal
Efficient frequent itemset mining methods over time-sensitive streams
Knowledge-Based Systems
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Mining frequent patterns over data streams is an interesting and challenging problem due to the emergence of new applications and limited resources of main memory and processing power. In this study, a novel sliding window based method for efficient mining of frequent patterns over data streams is proposed. This method provides a dynamic layout of sliding window by utilizing a set of simple lists for items existing within the window. For every item within the window, the most memory efficient list type based on its frequency is selected to store its occurrence information. A novel window adjustment technique including list type conversions is used to control the memory usage when the concept change occurs. At any time, if a user issues a request for frequent patterns in the recent window, a suitable approach based on the current content of the window is selected for the mining process. In comparison with recently proposed algorithms, empirical results show the superiority of the proposed method with multiple orders of magnitude in terms of runtime and memory usage.