Mining Low Dimensionality Data Streams of Continuous Attributes
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...
| Autores: | , , |
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| Tipo de recurso: | capítulo de libro |
| Estado: | Versión publicada |
| Fecha de publicación: | 2003 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/39235 |
| Acceso en línea: | http://hdl.handle.net/11441/39235 https://doi.org/10.1007/978-3-540-24580-3_33 |
| Access Level: | acceso abierto |
| Palabra clave: | Classification decision rules incremental learning scalable learning algorithms data streams |
| Sumario: | 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. |
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