DocumentCode
2283436
Title
A Multi-agents Approach to Knowledge Discovery
Author
Tong, Cuong ; Sharma, Dharmendra ; Shadabi, Fariba
Author_Institution
Fac. of Inf. Sci. & Eng., Univ. of Canberra, Canberra, ACT
Volume
3
fYear
2008
fDate
9-12 Dec. 2008
Firstpage
571
Lastpage
574
Abstract
Over the past few years, data mining and multi-agent approach has been used successfully in the development of large complex systems. Such a hybrid approach can be considered as an effective approach for the development of predictive modeling in complex e-health systems. We propose a real time Data Mining and Multi-Agent System called DMMAS, modeling chronic disease data. DMMAS approach employs data partitioning and multiple agents with option to employ heterogeneous or homogenous data mining techniques, distributing agent based processing for modeling and combining results from all the agents to improve the efficiency.
Keywords
biology computing; data mining; distributed processing; medical computing; multi-agent systems; chronic disease data modeling; complex e-health system; data partitioning; distributing agent based processing; heterogeneous mining; homogenous data mining; knowledge discovery; multiagent system; predictive modeling; Aging; Australia; Blood; Data mining; Databases; Diabetes; Diseases; Intelligent agent; Multiagent systems; Partitioning algorithms; Data mining; chronic disease; multi-agent;
fLanguage
English
Publisher
ieee
Conference_Titel
Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT '08. IEEE/WIC/ACM International Conference on
Conference_Location
Sydney, NSW
Print_ISBN
978-0-7695-3496-1
Type
conf
DOI
10.1109/WIIAT.2008.418
Filename
4740845
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