Mining low dimensionality data streams of continuous attributes

Abstract

This paper presents an incremental and scalable learning algorithm in order to mine numeric, low dimensionality, high-cardinality, time-changing data streams. Within the Supervised Learning field, our approach, named 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. © Springer-Verlag Berlin Heidelberg 2003.