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Agents and Stream Data Mining: A New Perspective
May/June 2005 (vol. 20 no. 3)
pp. 60-67
Kok-Leong Ong, Deakin University
Zili Zhang, Deakin University
Wee-Keong Ng, Nanyang Technological University
Ee-Peng Lim, Nanyang Technological University
Many organizations struggle with what to do with the massive amount of data they collect. Although some have touted data mining as the solution, it has failed to have a major impact despite its successes in many areas. One reason is that data mining algorithms weren't designed for the real world-that is, they usually assume a static view of the data and a stable execution environment with abundant resources. The reality, however, is that data constantly change and the execution environment is dynamic. So, it becomes difficult for data mining to truly deliver timely and relevant results. The solution to this might be to combine stream data mining algorithms with intelligent agents, as preliminary results from the Matrix project suggest.
Index Terms:
data mining, software agents, cooperative hybrid systems
Citation:
Kok-Leong Ong, Zili Zhang, Wee-Keong Ng, Ee-Peng Lim, "Agents and Stream Data Mining: A New Perspective," IEEE Intelligent Systems, vol. 20, no. 3, pp. 60-67, May-June 2005, doi:10.1109/MIS.2005.39
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