Memory-Aware Mining of Indirect Associations Over Data Streams
In this study, we focus on over a data stream the mining of indirect associations, a type of infrequent patterns that reveal infrequent itempairs yet highly co-occurring with a frequent itemset called “mediator”. We propose a generic framework MA-GIAMS, an extension of the GIAMS framework with memory-awareness capability that can cope with the variation of available memory space, making use of most available memory to accomplish the discovery of indirect association rules without incurring too much overhead and retaining as could as possible the accuracy of discovered rules. Empirical evaluations show that our algorithm can efficiently adjust the size of the data structure without sacrificing too much the accuracy of discovered indirect association rules.
KeywordsAdaptation scheme Indirect association Memory constraint Resource-awareness Stream mining
This work is partially supported by National Science Council of Taiwan under grant No. NSC97-2221-E-390-016-MY2.
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