Abstract
This paper presents an agent-based distributed data mining approach dealing with heterogeneous databases located at different sites. It introduces a modified decision tree algorithm on an agent based framework, which produces an accurate global model without transferring data between agents. The novel approach is evaluated over a test bed of texture feature data of 184 aerial photograph images. The experimental results show that the distributed version with more agents outperforms the version with fewer agents when the rule generation from the large database is not complicated.
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© 2004 Springer-Verlag Berlin Heidelberg
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Baik, S.W., Bala, J., Cho, J.S. (2004). Agent Based Distributed Data Mining. In: Liew, KM., Shen, H., See, S., Cai, W., Fan, P., Horiguchi, S. (eds) Parallel and Distributed Computing: Applications and Technologies. PDCAT 2004. Lecture Notes in Computer Science, vol 3320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30501-9_11
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DOI: https://doi.org/10.1007/978-3-540-30501-9_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24013-6
Online ISBN: 978-3-540-30501-9
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