Mining Data Streams under Dynamicly Changing Resource Constraints

AutorIn[nen]	Franke, Conny Karnstedt, Marcel Sattler, Kai-Uwe
In :	KDML 2006 :12. Workshop der Fachgruppe Knowledge Discovery, Data Mining und Maschinelles Lernen und des Arbeitskreises Knowledge Discovery ; (Hildesheim) : 2006.10.09-11 LWA 2006 : Lernen - Wissensentdeckung - Adaptivität (Workshop 9.11.10.2006 in Hildesheim) / Martin Schaaf, Klaus-Dieter Althoff [Hrsg.]	mehr...
	Universität Hildesheim, Institut für Informatik, 2006 (Tagungsbeitrag)
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Abstract/Inhalt	Due to the inherent characteristics of data streams, appropriate mining techniques heavily rely on window-based processing and/or (approximating) data summaries. Because resources such as memory and CPU time for maintaining such summaries are usually limited, the quality of the mining results is affected in different ways. Based on Frequent Itemset Mining and an according Change Detection as selected mining techniques, we discuss in this paper extensions of stream mining algorithms allowing to determine the output quality for changes in the available resources (mainly memory space). Furthermore, we give directions how to estimate resource consumptions based on user-specified quality requirements.