Distributed Nearest Neighbor-Based Condensation of Very Large Data Sets

In this work, the parallel fast condensed nearest neighbor (PFCNN) rule, a distributed method for computing a consistent subset of a 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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transactions on knowledge and data engineering Ročník 19; číslo 12; s. 1593 - 1606
Hlavní autori:	Angiulli, F., Folino, G.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.12.2007 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithm design and analysis Algorithms and association rules Cellular neural networks Classification Clustering Collection Computation Concurrent computing Condensing Cost analysis Costs Data mining Data reduction Distributed algorithms Distributed computing Distributed systems Grid computing Nearest neighbor searches Neural networks Clustering classification Data mining Distributed systems and association rules
ISSN:	1041-4347, 1558-2191
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!