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A Human-Friendly MAS for Mining Stock Data

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2 Author(s)
Jiarui Ni ; Univ. of Technol., Sydney, NSW ; Zhang, C.

Mining stock data can be beneficial to the participants and researchers in the stock market. However, it is very difficult for a normal trader or researcher to apply data mining techniques to the data on his own due to the complexity involved in the whole data mining process. In this paper, we present a multi-agent system that can help users easily deal with their data mining jobs on stock data. This system guides users to specify their mining tasks by simply specifying the data sets to be mined and selecting pre-defined and/or user-added data mining agents. This approach offers normal traders a practical and flexible solution to mining stock data

Published in:

Web Intelligence and Intelligent Agent Technology Workshops, 2006. WI-IAT 2006 Workshops. 2006 IEEE/WIC/ACM International Conference on

Date of Conference:

Dec. 2006

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