ACM Transactions on Information Systems (TOIS)
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient Mining of Intertransaction Association Rules
IEEE Transactions on Knowledge and Data Engineering
Efficiently Mining Maximal Frequent Itemsets
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A template model for multidimensional inter-transactional association rules
The VLDB Journal — The International Journal 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
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
An efficient algorithm for mining frequent inter-transaction patterns
Information Sciences: an International Journal
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
IFCIA: An Efficient Algorithm for Mining Intertransaction Frequent Closed Itemsets
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
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
An efficient algorithm for mining closed inter-transaction itemsets
Data & Knowledge Engineering
Approximate mining of maximal frequent itemsets in data streams with different window models
Expert Systems with Applications: An International Journal
ICCSIT '08 Proceedings of the 2008 International Conference on Computer Science and Information Technology
Mining polyphonic repeating patterns from music data using bit-string based approaches
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Information Sciences: an International Journal
Online mining maximal frequent structures in continuous landmark melody streams
Pattern Recognition Letters
Efficient mining of cross-transaction web usage patterns in large database
ICCNMC'05 Proceedings of the Third international conference on Networking and Mobile Computing
MHUI-max: An efficient algorithm for discovering high-utility itemsets from data streams
Journal of Information Science
Mining frequent patterns in a varying-size sliding window of online transactional data streams
Information Sciences: an International Journal
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Mining inter-transaction association rules is one of the most interesting issues in data mining research. However, in a data stream environment the previous approaches are unable to find the result of the new-incoming data and the original database without re-computing the whole database. In this paper, we propose an incremental mining algorithm, called DSM-CITI (Data Stream Mining for Closed Inter-Transaction Itemsets), for discovering the set of all frequent inter-transaction itemsets from data streams. In the framework of DSM-CITI, a new in-memory summary data structure, ITP-tree, is developed to maintain frequent inter-transaction itemsets. Moreover, algorithm DSM-CITI is able to construct ITP-tree incrementally and uses the property to avoid unnecessary updates. Experimental studies show that the proposed algorithm is efficient and scalable for mining frequent inter-transaction itemsets over stream sliding windows.