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
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Efficient Mining of Weighted Frequent Patterns over Data Streams
HPCC '09 Proceedings of the 2009 11th IEEE International Conference on High Performance Computing and Communications
Valency based weighted association rule mining
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Research inWeighted Association Rule Mining (WARM) has largely concentrated on mining traditional static transactional datasets. Whilst there have been a few attempts at researching WARM in a data stream environment, none have addressed the problem of assigning and adapting weights in the presence of concept drift, which often occurs in a data stream environment. In this research we experiment with two methods of adapting weights; firstly, a simplistic method that recomputes the entire set of weights at fixed intervals, and secondly a method that relies on a distance function that assesses the extent of change in the stream and only updates those items that have had significant change in their patterns of interaction. We show that the latter method is able to maintain good accuracy whilst being several times faster than the former.