Mining data streams under block evolution
ACM SIGKDD Explorations Newsletter
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Identifying frequent items in sliding windows over on-line packet streams
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
ACM SIGMOD Record
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Find Recent Frequent Items with Sliding Windows in Data Streams
IIH-MSP '07 Proceedings of the Third International Conference on International Information Hiding and Multimedia Signal Processing (IIH-MSP 2007) - Volume 02
Approximate mining of frequent patterns on streams
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Verifying and Mining Frequent Patterns from Large Windows over Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A landmark-model based system for mining frequent patterns from uncertain data streams
Proceedings of the 15th Symposium on International Database Engineering & Applications
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Traditional algorithms for frequent itemset discovery are designed for static data. They cannot be straightforwardly applied to data streams which are continuous, unbounded, usually coming at high speed and often with a data distribution which changes with time. The main challenges of frequent pattern mining in data streams are: avoiding multiple scans of the entire dataset, optimizing memory usage and capturing distribution drift. To face these challenges, we propose a novel algorithm, which is based on a sliding window model in order to deal with efficiency issues and to keep up with distribution change. Each window consists of several slides. The generation of itemsets is local to each slide, while the estimation of their approximate support is based on the window. Efficiency in the generation of the itemsets is ensured by the usage of a synopsis structure, called SE-tree. Experiments prove the effectiveness of the proposed algorithm.