Mining Low Dimensionality Data Streams of Continuous Attributes

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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.

Francisco J. Ferrer-Troyano, Jesús S. Aguil

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Data Streams | Diﬀerent Complexity Degrees | EPIA 2003 | Scalable Learning Algorithm |

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Added	06 Jul 2010
Updated	06 Jul 2010
Type	Conference
Year	2003
Where	EPIA
Authors	Francisco J. Ferrer-Troyano, Jesús S. Aguilar-Ruiz, José Cristóbal Riquelme Santos

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Mining Low Dimensionality Data Streams of Continuous Attributes

Data Streams | Diﬀerent Complexity Degrees | EPIA 2003 | Scalable Learning Algorithm |

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