A Multi-Agents Approach to Knowledge Discovery

Cuong Tong, Dharmendra Sharma, Fariba Shadabi

Research output: A Conference proceeding or a Chapter in BookConference contribution

6 Citations (Scopus)

Abstract

Over the past few years, data mining and multiagent 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.

Original languageEnglish
Title of host publicationProceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008
EditorsYuefeng Li, Gabriella Pasi, Chengqi Zhang, Nick Cercone, Longbing Cao
Place of PublicationPiscataway, USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages571-574
Number of pages4
ISBN (Print)9780769534961
DOIs
Publication statusPublished - 2008
Event2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008 - Sydney, NSW, Australia
Duration: 9 Dec 200812 Dec 2008

Conference

Conference2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008
CountryAustralia
CitySydney, NSW
Period9/12/0812/12/08

Fingerprint

Data mining
Multi agent systems
Large scale systems
Health
Processing

Cite this

Tong, C., Sharma, D., & Shadabi, F. (2008). A Multi-Agents Approach to Knowledge Discovery. In Y. Li, G. Pasi, C. Zhang, N. Cercone, & L. Cao (Eds.), Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008 (pp. 571-574). Piscataway, USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/WIIAT.2008.418
Tong, Cuong ; Sharma, Dharmendra ; Shadabi, Fariba. / A Multi-Agents Approach to Knowledge Discovery. Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008. editor / Yuefeng Li ; Gabriella Pasi ; Chengqi Zhang ; Nick Cercone ; Longbing Cao. Piscataway, USA : IEEE, Institute of Electrical and Electronics Engineers, 2008. pp. 571-574
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Tong, C, Sharma, D & Shadabi, F 2008, A Multi-Agents Approach to Knowledge Discovery. in Y Li, G Pasi, C Zhang, N Cercone & L Cao (eds), Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008. IEEE, Institute of Electrical and Electronics Engineers, Piscataway, USA, pp. 571-574, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008, Sydney, NSW, Australia, 9/12/08. https://doi.org/10.1109/WIIAT.2008.418

A Multi-Agents Approach to Knowledge Discovery. / Tong, Cuong; Sharma, Dharmendra; Shadabi, Fariba.

Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008. ed. / Yuefeng Li; Gabriella Pasi; Chengqi Zhang; Nick Cercone; Longbing Cao. Piscataway, USA : IEEE, Institute of Electrical and Electronics Engineers, 2008. p. 571-574.

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

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AU - Sharma, Dharmendra

AU - Shadabi, Fariba

PY - 2008

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N2 - Over the past few years, data mining and multiagent 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.

AB - Over the past few years, data mining and multiagent 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.

KW - Data mining

KW - multi-agent

KW - chronic disease

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U2 - 10.1109/WIIAT.2008.418

DO - 10.1109/WIIAT.2008.418

M3 - Conference contribution

SN - 9780769534961

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BT - Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008

A2 - Li, Yuefeng

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A2 - Cercone, Nick

A2 - Cao, Longbing

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway, USA

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Tong C, Sharma D, Shadabi F. A Multi-Agents Approach to Knowledge Discovery. In Li Y, Pasi G, Zhang C, Cercone N, Cao L, editors, Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008. Piscataway, USA: IEEE, Institute of Electrical and Electronics Engineers. 2008. p. 571-574 https://doi.org/10.1109/WIIAT.2008.418