Borders: An Efficient Algorithm for Association Generation in Dynamic Databases
Journal of Intelligent Information Systems
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining data streams under block evolution
ACM SIGKDD Explorations Newsletter
DEMON: Mining and Monitoring Evolving Data
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Efficiently Mining Maximal Frequent Itemsets
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Pushing Convertible Constraints in Frequent Itemset Mining
Data Mining and Knowledge Discovery
Mining Recent Frequent Itemsets in Data Streams
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
An Efficient Frequent Pattern Mining Algorithm for Data Stream
ICICTA '08 Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation - Volume 01
IMBT--A Binary Tree for Efficient Support Counting of Incremental Data Mining
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 01
Constrained itemset mining on a sequence of incoming data blocks
International Journal of Intelligent Systems
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This paper presents a new approach to efficiently discovering correlations among data items on a sequence of incoming data windows. The approach enables both on-line (e.g., mining only the most recent data) and off-line (e.g., analyzing aggregate data windows) queries, besides supporting user-defined item and support constraints. Given a sequence of transactional data windows and a minimum support threshold, for each of the most recent data windows a projection is compactly stored in main-memory, including all items that have been frequently observed in the last windows. Users can easily perform constrained itemset extraction either from a single data window or from multiple ones. A summary of interesting itemsets mined from all available data is generated on a regular basis and compactly stored in a persistent data structure, to efficiently support further analysis (e.g., investigate only a selected past data window). Experimental results obtained on both real and synthetic data streams show the effectiveness and the efficiency of the proposed approach in mining interesting itemsets by means of both on-line and off-line queries.