A Multi-Agents Approach to Knowledge Discovery

Cuong Tong; Dharmendra Sharma; Fariba Shadabi

doi:10.1109/WIIAT.2008.418

A Multi-Agents Approach to Knowledge Discovery

Cuong Tong, Dharmendra Sharma, Fariba Shadabi

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

7 Citations (Scopus)

121 Downloads (Pure)

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 language	English
Title of host publication	Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008
Editors	Yuefeng Li, Gabriella Pasi, Chengqi Zhang, Nick Cercone, Longbing Cao
Place of Publication	Piscataway, USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	571-574
Number of pages	4
ISBN (Print)	9780769534961
DOIs	https://doi.org/10.1109/WIIAT.2008.418
Publication status	Published - 2008
Event	2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008 - Sydney, NSW, Australia Duration: 9 Dec 2008 → 12 Dec 2008

Conference

Conference	2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008
Country/Territory	Australia
City	Sydney, NSW
Period	9/12/08 → 12/12/08

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10.1109/WIIAT.2008.418

Conference PaperFinal published version, 189 KBLicence: Other

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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 (pp. 571-574). 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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title = "A Multi-Agents Approach to Knowledge Discovery",

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.",

keywords = "Data mining, multi-agent, chronic disease",

author = "Cuong Tong and Dharmendra Sharma and Fariba Shadabi",

year = "2008",

doi = "10.1109/WIIAT.2008.418",

language = "English",

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pages = "571--574",

editor = "Yuefeng Li and Gabriella Pasi and Chengqi Zhang and Nick Cercone and Longbing Cao",

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

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008 ; Conference date: 09-12-2008 Through 12-12-2008",

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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 Book › Conference contribution › peer-review

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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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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

T2 - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008

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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 doi: 10.1109/WIIAT.2008.418

A Multi-Agents Approach to Knowledge Discovery

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