EPIA'03 - 11th Portuguese Conference on Artificial Intelligence

	Session: December 5, 14:45-16:15, Room A
Title:	Mining Low Dimensionality Data Streams of Continuous Attributes
	Francisco J. Ferrer--Troyano, Jesus S. Aguilar--Ruiz, Jose C. Riquelme, Domingo S. Rodriguez
Abstract:	This paper presents an incremental and scalable learning algorithm, called SCALLOP, in order to mine numeric, low dimensionality, high--cardinality, time--changing data streams. Within Supervised Learning, SCALLOP provides a set of decision rules whose size is very near to the number of concepts to be extracted. Experimental results with synthetic databases of different complexity degrees show a good performance from streams of data received at a rapid rate whose label distribution may not be stationary in time.

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