Abstract
An indirect association refers to an infrequent itempair, each item of which is highly co-occurring with a frequent itemset called “mediator”. Although indirect associations have been recognized as powerful patterns in revealing interesting information hidden in many applications, such as recommendation ranking, substitute items or competitive items, and common web navigation path, etc., almost no work, to our knowledge, has investigated how to discover this type of patterns from streaming data. In this paper, the problem of mining indirect associations from data streams is considered. Unlike contemporary research work on stream data mining that investigates the problem individually from different types of streaming models, we treat the problem in a generic way. We propose a generic window model that can represent all classical streaming models and retain user flexibility in defining new models. In this context, a generic algorithm is developed, which guarantees no false positive rules and bounded support error as long as the window model is specifiable by the proposed generic model. Comprehensive experiments on both synthetic and real datasets have showed the effectiveness of the proposed approach as a generic way for finding indirect association rules over streaming data.
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Lin, WY., Wei, YE., Chen, CH. (2011). A Generic Approach for Mining Indirect Association Rules in Data Streams. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6703. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21822-4_11
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DOI: https://doi.org/10.1007/978-3-642-21822-4_11
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