DocumentCode :
2834797
Title :
A Framework of an Automated Data Mining System Using Autonomous Intelligent Agents
Author :
Rajan, J. ; Saravanan, V.
Author_Institution :
Dept. of Comput. Applic., Karunya Univ., Coimbatore
fYear :
2008
fDate :
Aug. 29 2008-Sept. 2 2008
Firstpage :
700
Lastpage :
704
Abstract :
Data mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner. In other words data mining is a process of finding previously unknown, profitable and use patterns hidden in data, with no prior hypothesis. Automated Data Mining and modeling software gives managers a tool to perform analyses that otherwise would need to be handled by a highly trained researcher. Automated data mining methodologies is not to provide more accurate results but strives to empower non-expert users to achieve reasonable results with minimum effort. Data mining is a difficult and laborious activity that requires a great deal of expertise for obtaining quality results. We need new methods for intelligent data analysis to extract relevant information with less effort. With the use of the autonomous intelligent agents several data mining steps are possibly be automated. The goal is to empower non-expert users to achieve reasonable results with minimum effort. In this paper we present an automated approach for a data mining system using autonomous intelligent agents.
Keywords :
data mining; software agents; automated data mining system; autonomous intelligent agents; data analysis; Application software; Computer applications; Computer science; Data mining; Databases; Information analysis; Information technology; Intelligent agent; Software performance; Software tools; Automated; Data Mining; Intelligent Agents;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Science and Information Technology, 2008. ICCSIT '08. International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-0-7695-3308-7
Type :
conf
DOI :
10.1109/ICCSIT.2008.167
Filename :
4624958
Link To Document :
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