Efficient Mining of Frequent Itemsets from Data Streams
BNCOD '08 Proceedings of the 25th British national conference on Databases: Sharing Data, Information and Knowledge
IDEAS '08 Proceedings of the 2008 international symposium on Database engineering & applications
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
Mining uncertain data for constrained frequent sets
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Constrained frequent pattern mining on univariate uncertain data
Journal of Systems and Software
Pushing constraints into data streams
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
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With advances in technology, a flood of data can be produced in many applications such as sensor networks and Web click streams. This calls for stream mining, which searches for implicit, previously unknown, and potentially useful information (such as frequent patterns) that might be embedded in continuous data streams. However, most of the existing algorithms do not allow users to express the patterns to be mined according to their intentions, via the use of constraints. Consequently, these unconstrained mining algorithms can yield numerous patterns that are not interesting to users. In this paper, we develop algorithms- which use a tree-based framework to capture the important portion of the streaming data, and allow human users to impose a certain focus on the mining process- for mining frequent patterns that satisfy user constraints from the flood of data.