Streaming Data Clustering in MOA using the Leader Algorithm
This master thesis presents a novel stream clustering algorithm, called StreamLeader. It presents a way to deliver clustering without the need of resorting to conventional clustering algorithms, like most other algorithms do. We test it, outperforming its state of the art rivals in most of the cases
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/79235 |
| Acceso en línea: | https://hdl.handle.net/2117/79235 |
| Access Level: | acceso abierto |
| Palabra clave: | Computer algorithms StreamLeader LeaderKernel stream clustering MOA leader Hartigan Clustream Denstream Clustree Network Intrusion Forest Cover Type dimensionality noise Algorismes computacionals Àrees temàtiques de la UPC::Informàtica |
| Sumario: | This master thesis presents a novel stream clustering algorithm, called StreamLeader. It presents a way to deliver clustering without the need of resorting to conventional clustering algorithms, like most other algorithms do. We test it, outperforming its state of the art rivals in most of the cases |
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