Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Sliding-window filtering: an efficient algorithm for incremental mining
Proceedings of the tenth international conference on Information and knowledge management
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
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
Issues in data stream management
ACM SIGMOD Record
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamically maintaining frequent items over a data stream
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on 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
A fast high utility itemsets mining algorithm
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Isolated items discarding strategy for discovering high utility itemsets
Data & Knowledge Engineering
An efficient algorithm for mining temporal high utility itemsets from data streams
Journal of Systems and Software
DSM-FI: an efficient algorithm for mining frequent itemsets in data streams
Knowledge and Information Systems
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
Incremental updates of closed frequent itemsets over continuous data streams
Expert Systems with Applications: An International Journal
Online mining of temporal maximal utility itemsets from data streams
Proceedings of the 2010 ACM Symposium on Applied Computing
TOPSIL-Miner: an efficient algorithm for mining top-K significant itemsets over data streams
Knowledge and Information Systems
Efficient monitoring of skyline queries over distributed data streams
Knowledge and Information Systems
Incremental mining of closed inter-transaction itemsets over data stream sliding windows
Journal of Information Science
Knowledge and Information Systems - Special Issue on Data Warehousing and Knowledge Discovery from Sensors and Streams
Sliding window based weighted maximal frequent pattern mining over data streams
Expert Systems with Applications: An International Journal
Mining high utility itemsets by dynamically pruning the tree structure
Applied Intelligence
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Online mining of utility itemsets from data streams is one of the most interesting research issues in stream data mining. Although a number of relevant approaches have been proposed in recent years, they have the drawback of producing a large number of candidate itemsets for high-utility itemset mining. In this paper, an efficient algorithm, called MHUI-max (Mining High-Utility Itemsets based on LexTree-maxHTU), is proposed for mining high-utility itemsets from data streams with fewer candidates. Based on the framework of the MHUI-max algorithm, an effective representation of item information, called TID-list, and a new lexicographical tree-based data structure, called LexTree-maxHTU, has been developed to improve the efficiency of discovering high-utility itemsets with positive profits from data streams. Experimental results show that the proposed algorithm, MHUI-max, outperforms the existing approaches, MHUI-TID and THUI-Mine, for mining high-utility itemsets from data streams over transaction-sensitive sliding windows.