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...

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Veröffentlicht in:IEEE transactions on knowledge and data engineering Jg. 19; H. 12; S. 1593 - 1606
Hauptverfasser: Angiulli, F., Folino, G.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.12.2007
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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 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.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2007.190665