A hybrid model for optimising distributed data mining

Krishnaswamy, Shonali, Zaslavsky, Arkady and Loke, Seng Wai 2004, A hybrid model for optimising distributed data mining, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2918, pp. 300-310.


Title A hybrid model for optimising distributed data mining
Author(s) Krishnaswamy, Shonali
Zaslavsky, ArkadyORCID iD for Zaslavsky, Arkady orcid.org/0000-0003-1990-5734
Loke, Seng WaiORCID iD for Loke, Seng Wai orcid.org/0000-0001-9568-5230
Journal name Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume number 2918
Start page 300
End page 310
Total pages 11
Publisher Springer
Publication date 2004-12-01
ISSN 0302-9743
1611-3349
Keyword(s) Science & Technology
Technology
Computer Science, Hardware & Architecture
Computer Science, Theory & Methods
Computer Science
Summary This paper presents a hybrid model for improving the response time of distributed data mining (DDM). The hybrid DDM model uses cost formulae and prediction techniques to compute an estimate of the response time for a DDM process and applies a combination of client-server and mobile agent strategies based on the estimates to reduce the overall response time. Experimental results that establish the validity and demonstrate the improved response time of the hybrid model are presented.
Language eng
Indigenous content off
Field of Research 08 Information and Computing Sciences
Persistent URL http://hdl.handle.net/10536/DRO/DU:30126839

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