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
Pincer Search: A New Algorithm for Discovering the Maximum Frequent Set
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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
Mining itemset utilities from transaction databases
Data & Knowledge Engineering - Special issue: ER 2003
High-utility pattern mining: A method for discovery of high-utility item sets
Pattern Recognition
CTU-Mine: An Efficient High Utility Itemset Mining Algorithm Using the Pattern Growth Approach
CIT '07 Proceedings of the 7th IEEE International Conference on Computer and Information Technology
Isolated items discarding strategy for discovering high utility itemsets
Data & Knowledge Engineering
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
Fast and Memory Efficient Mining of High Utility Itemsets in Data Streams
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Mining frequent itemsets in data streams using the weighted sliding window model
Expert Systems with Applications: An International Journal
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases
IEEE Transactions on Knowledge and Data Engineering
UP-Growth: an efficient algorithm for high utility itemset mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
An effective tree structure for mining high utility itemsets
Expert Systems with Applications: An International Journal
EclatDS: An efficient sliding window based frequent pattern mining method for data streams
Intelligent Data Analysis
A two-phase algorithm for fast discovery of high utility itemsets
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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High utility itemsets mining is a hot topic in data stream mining. It is essential that the mining algorithm should be efficient in both time and space for data stream is continuous and unbounded. To the best of our knowledge, the existing algorithms require multiple database scans to mine high utility itemsets, and this hinders their efficiency. In this paper, we propose a new data structure, called UT-Tree Utility on Tail Tree, for maintaining utility information of transaction itemsets to avoid multiple database scans. The UT-Tree is created with one database scan, and contains a fixed number of transaction itemsets; utility information is stored on tail-nodes only. Based on the proposed data structure and the sliding window approach, we propose a mining algorithm, called HUM-UT High Utility itemsets Mining based on UT-Tree, to find high utility itemsets from transactional data streams. The HUM-UT algorithm mines high utility itemsets from the UT-Tree without additional database scan. Experiment results show that our algorithm has better performance and is more stable under different experimental conditions than the state-of-the-art algorithm HUPMS in terms of time and space.