Multi-Agent Architecture for Knowledge Discovery

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	Similar Articles Articles by Daniel Pop Articles by Viorel Negru Articles by Calin Sandru

Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'06)

Timisoara, Romania

September 26-September 29

ISBN: 0-7695-2740-X

	ASCII Text	x
	Daniel Pop, Viorel Negru, Calin Sandru, "Multi-Agent Architecture for Knowledge Discovery," Symbolic and Numeric Algorithms for Scientific Computing, International Symposium on, pp. 217-226, Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'06), 2006.

	BibTex	x
	@article{ 10.1109/SYNASC.2006.55, author = {Daniel Pop and Viorel Negru and Calin Sandru}, title = {Multi-Agent Architecture for Knowledge Discovery}, journal ={Symbolic and Numeric Algorithms for Scientific Computing, International Symposium on}, volume = {0}, year = {2006}, isbn = {0-7695-2740-X}, pages = {217-226}, doi = {http://doi.ieeecomputersociety.org/10.1109/SYNASC.2006.55}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Symbolic and Numeric Algorithms for Scientific Computing, International Symposium on TI - Multi-Agent Architecture for Knowledge Discovery SN - 0-7695-2740-X SP217 EP226 A1 - Daniel Pop, A1 - Viorel Negru, A1 - Calin Sandru, PY - 2006 KW - null VL - 0 JA - Symbolic and Numeric Algorithms for Scientific Computing, International Symposium on ER -

Daniel Pop, West University of Timisoara, Romania

Viorel Negru, West University of Timisoara, Romania

Calin Sandru, West University of Timisoara, Romania

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SYNASC.2006.55

Knowledge Discovery from Databases (KDD) is a complex process composed of several phases: business understanding, data understanding, data preparation, modeling, evaluation and deployment. For each of the phases, there are many algorithms and methods available, the end-user having to select one of them. The AgentDiscover is a multi-agent based intelligent recommendation system for selection of the most appropriate solving method for each phase. This brings added value for both novice and experienced users.

Citation:

Daniel Pop, Viorel Negru, Calin Sandru, "Multi-Agent Architecture for Knowledge Discovery," synasc, pp.217-226, Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'06), 2006

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