IJSEA Archive (Volume 2, Issue 5)
International Journal of Science and Engineering Applications (IJSEA) (Volume 2, Issue 5 - May 2013)
Agent-Driven Distributed Data Mining
Keywords: Distributed Data Mining, Multi-Agent Systems, Multi Agent Data Mining,Multi-Agent Based Distributed Data Mining.
Multi-Agent systems (Autonomous agents or agents) and knowledge discovery (or data mining) are two
active areas in information technology. A profound insight of bringing these two communities together has
unveiled a tremendous potential for new opportunities and wider applications through the synergy of agents and
data mining. Multi-agent systems (MAS) often deal with complex applications that require distributed problem
solving. In many applications the individual and collective behavior of the agents depends on the observed data
from distributed data sources. Data mining technology has emerged, for identifying patterns and trends from
large quantities of data. The increasing demand to scale up to massive data sets inherently distributed over a
network with limited band width and computational resources available motivated the development of distributed
data mining (DDM).Distributed data mining is originated from the need of mining over decentralized data
sources. DDM is expected to perform partial analysis of data at individual sites and then to send the outcome as
partial result to other sites where it sometimes required to be aggregated to the global result.
[1] T.C. Du, E.Y. Li, and A. Chang,―Mobile agents in distributed network management,‖ Commun. ACM, vol. 46, no.
7, pp. 127–132, July 2003.
[2] H. Ku, G.W.R. Ludere, and B.Subbiah, ―An intelligent mobile agent framework for distributed network
management,‖in Proc. Globecom’97Phoenix, pp. 160–164.
[3] N. R. Jennings and S. Bussmann, ―Agent-based control systems—Why are they suited to engineering
complex systems?‖ IEEE Control Syst.Mag., vol. 23, no. 3, pp. 61–73, Jun. 2003.
[4] R. B. Patel, Neeraj Goel, ―Mobile Agents in Heterogeneous Networks: A Look on Performance,‖
Journal of Computer Science,2(11): 824-834, 2006.
[5] O'Hare G.M.P., Marsh D., Ruzzelli A., R. Tynan,―Agents for Wireless Sensor Network
Power Management‖, in Proceedings of International Workshop on Wireless and Sensor Networks (WSNET-05), Oslo,
Norway IEEE Press, 2005.
[6] S. Bailey, R. Grossman, H. Sivakumar, and A. Turinsky. Papyrus: a system for data mining over local
and wide area clusters and super-clusters. In Supercomputing ’99: Proceedings of the 1999 ACM/IEEE conference on
Supercomputing (CDROM), page 63, New York, NY, USA, 1999. ACM
[7] R. J. Bayardo, W. Bohrer, R. Brice, A.Cichocki, J. Fowler, A. Helal, V. Kashyap, T. Ksiezyk, G.
Martin, M. Nodine, and Others.InfoSleuth: agent-based semantic integration of information in open and dynamic
environments. ACM SIGMOD Record, 26(2):195–206, 1997.
[8] F. Bergenti, M.P. Gleizes, and F.Zambonelli.
Methodologies And Software Engineering For Agent Systems: The Agent oriented Software Engineering Handbook.
Kluwer Academic Publishers, 2004.
[9] R. Bose and V. Sugumaran. IDM: an intelligent software agent based data mining environment. 1998 IEEE
International Conference on Systems, Man, and Cybernetics, 3, 1998.
[10] J. Dasilva, C. Giannella, R. Bhargava, H.Kargupta, and M. Klusch. Distributed datamining and agents.
Engineering Applicationsof Artificial Intelligence, 18(7):791–807,
October 2005.
[11] S. Datta, K. Bhaduri, C. Giannella, R. Wolff,and H. Kargupta. Distributed data mining in peer-to-peer
networks. Internet Computing,IEEE, 10(4):18–26, 2006.
[12] U. Fayyad, R. Uthurusamy, and Others. Data mining and knowledge discovery in databases.
Communications of the ACM,39(11):24–26, 1996.
[13] Vladimir Gorodetsky, Oleg Karsaev, and Vladimir Samoilov. Multi-agent technology for distributed data
mining and classification.In IAT, pages 438–441. IEEE Computer
Society, 2003.
[14] Sven A. Brueckner H. Van Dyke Parunak. Engineering swarming systems.Methodologies and Software Engineering
for Agent Systems, pages 341–376, 2004.
[15] W. Davies and P. Edwards. Distributed Learning: An Agent-Based Approach to Data-Mining. In
Proceedings of MachineLearning 95 Workshop on Agents that Learn from Other Agents, 1995.
@article{rohini02051003,
title = "Monitoring the Respiratory System using Temperature Sensor ",
journal = "International Journal of Science and Engineering Applications (IJSEA)",
volume = "2",
number = "5",
pages = "103 - 109",
year = "2013",
author = "Rohini. P, Sree Lakshmi.P ",
}