Future Work

The study of mining indirect associations from streaming data is in its infancy. Many research issues are worthy of further investigation. First, we will continue to improve the efficiency of the proposed algorithms, seeking more effective way for reducing the memory usage. We will also extend the proposed algorithms to different window models such as time-fading model and sliding-window model. Recently, the design of adaptive data stream mining methods that can perform adaptively under constrained resources has emerged into an important and challenging research issue to the data mining community. In the future, we will study how to apply or incorporate some adaptive technique such as load shedding into our approach, especially when the situation is that we have very limited resources, such as CPU computing power or memory size, without sacrificing too much the quality of the discovered indirect associations.

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