Title :
A Human-Friendly MAS for Mining Stock Data
Author :
Ni, Jiarui ; Zhang, Chengqi
Author_Institution :
Univ. of Technol., Sydney, NSW
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
Keywords :
data mining; multi-agent systems; stock markets; data mining techniques; mining stock data; multi-agent system; stock market; Australia; Computer industry; Consumer electronics; Data mining; Event detection; Information technology; Multiagent systems; Stock markets; Synthetic aperture sonar; Time factors;
Conference_Titel :
Web Intelligence and Intelligent Agent Technology Workshops, 2006. WI-IAT 2006 Workshops. 2006 IEEE/WIC/ACM International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2749-3
DOI :
10.1109/WI-IATW.2006.12
