An agent-based hybrid framework for database mining

Zhang, Zili, Zhang, Chengqi and Zhang, Shichao 2003, An agent-based hybrid framework for database mining, Applied artificial intelligence, vol. 17, no. 5&6, pp. 383-398, doi: 10.1080/713827179.

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Title An agent-based hybrid framework for database mining
Author(s) Zhang, ZiliORCID iD for Zhang, Zili
Zhang, Chengqi
Zhang, Shichao
Journal name Applied artificial intelligence
Volume number 17
Issue number 5&6
Start page 383
End page 398
Publisher Taylor and Francis Inc
Place of publication Washington, D.C., Wash.
Publication date 2003-05
ISSN 0883-9514
Summary While knowledge discovery in databases (KDD) is defined as an iterative sequence of the following steps: data pre-processing, data mining, and post data mining, a significant amount of research in data mining has been done, resulting in a variety of algorithms and techniques for each step. However, a single data-mining technique has not been proven appropriate for every domain and data set. Instead, several techniques may need to be integrated into hybrid systems and used cooperatively during a particular data-mining operation. That is, hybrid solutions are crucial for the success of data mining. This paper presents a hybrid framework for identifying patterns from databases or multi-databases. The framework integrates these techniques for mining tasks from an agent point of view. Based on the experiments conducted, putting different KDD techniques together into the agent-based architecture enables them to be used cooperatively when needed. The proposed framework provides a highly flexible and robust data-mining platform and the resulting systems demonstrate emergent behaviors although it does not improve the performance of individual KDD techniques.
Language eng
DOI 10.1080/713827179
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2003, Taylor & Francis
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