A Human-Friendly MAS for Mining Stock Data

Authors:
Jiarui Ni;Chengqi Zhang
Affiliations:
University of Technology, Sydney, Australia;University of Technology, Sydney, Australia
Venue:
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Year:
2006

Citing 2
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A Distributed Multi-agent System implementation of the Human-Friendly MAS for Mining Stock Data

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Abstract

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.