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Why is it important?
The proposed framework can represent all classical streaming models and retain user flexibility in defining new models. Our algorithm guarantees no false positive rules and bounded support errors as long as the window model is specifiable by the proposed generic model.
Perspectives
To the best of our knowledge, this is the first research work on mining indirect associations from stream data. Unlike contemporary research work on stream data mining that has investigated the problem by looking at some specific streaming model, we treat the problem in a generic way. Our proposed framework also can be applied to discover other types of patterns, such as frequent itemsets, association rules, classification rules.
Prof. Wen-Yang Lin
National University of Kaohsiung
Read the Original
This page is a summary of: GIAMS: A generic approach for mining indirect association rules in data streams1, Intelligent Data Analysis, April 2017, IOS Press,
DOI: 10.3233/ida-170877.
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