|
Descripción
|
|
|---|---|
| he quality of grid-based subspace clustering is highly depen- dent on the grid size and the positions of dense units, and many existing methods use sensitive global density thresholds that are difficult to set a priori. We propose PSCEG, a new approach that generates an exact grid without the need to specify its size based on the distribution of each di- mension. In addition, we define an adaptive density estimator that avoids dimensionality bias. A parallel implementation of our algorithm using Resilient Distributed Datasets achieves a significant speedup w.r.t. the number of cores in high dimensional scenarios. Experimental results on synthetic and real datasets show PSCEG outperforms existing alternatives. | |
|
Internacional
|
Si |
|
Nombre congreso
|
ESANN, 2016 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - ESANN (Alpha Core Ranking: CORE B). |
|
Tipo de participación
|
960 |
|
Lugar del congreso
|
Bruges, Belgium. |
|
Revisores
|
Si |
|
ISBN o ISSN
|
978-287587027-8 |
|
DOI
|
|
|
Fecha inicio congreso
|
27/04/2016 |
|
Fecha fin congreso
|
29/04/2016 |
|
Desde la página
|
581 |
|
Hasta la página
|
586 |
|
Título de las actas
|
ESANN 2016 proceedings, European 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - ESANN . |