http://www.cnr.it/ontology/cnr/individuo/prodotto/ID14505
Distributed Nearest Neighbor Based Condensation of Very Large Datasets (Articolo in rivista)
- Type
- Label
- Distributed Nearest Neighbor Based Condensation of Very Large Datasets (Articolo in rivista) (literal)
- Anno
- 2007-01-01T00:00:00+01:00 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Angiulli Fabrizio, Folino Gianluigi (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Note
- DBLP (literal)
- Scopu (literal)
- ISI Web of Science (WOS) (literal)
- Google Scholar (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- DEIS, Università della Calabria
Istituto di calcolo e reti ad alte prestazioni (literal)
- Titolo
- Distributed Nearest Neighbor Based Condensation of Very Large Datasets (literal)
- Abstract
- In this work, PFCNN, a distributed method for computing
a consistent subset of very large data set for the nearest
neighbor classification rule is presented. In order to cope with the
communication overhead typical of distributed environments and
to reduce memory requirements, different variants of the basic
PFCNN method are introduced. An analysis of spatial cost, CPU
cost, and communication overhead is accomplished for all the
algorithms. Experimental results, performed on both synthetic
and real very large data sets, revealed that these methods can
be profitably applied to enormous collections of data. Indeed,
they scale-up well and are efficient in memory consumption,
confirming the theoretical analysis, and achieve noticeable data
reduction and good classification accuracy. To the best of our
knowledge, this is the first distributed algorithm for computing
a training set consistent subset for the nearest neighbor rule. (literal)
- Prodotto di
- Autore CNR
Incoming links:
- Prodotto
- Autore CNR di
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi
