Tracking the Intrinsic Dimension of Evolving Data Streams to Update Association Rules
نویسندگان
چکیده
Data streams can change their behavior over time and, when a significant change occurs, the rules governing the attributes reported by each event can also change. Moreover, data streams can be composed of events from several classes, and the rules governing the events of each class can also change depending on actual properties of the data. In this paper we propose a new technique to continuously identify which are the most relevant attributes to characterize each class, based on the general properties exhibited by the data stream as it evolves over time.
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