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Proceedings Paper

Classification data mining method based on dynamic RBF neural networks
Author(s): Lijuan Zhou; Min Xu; Zhang Zhang; Luping Duan
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Paper Abstract

With the widely application of databases and sharp development of Internet, The capacity of utilizing information technology to manufacture and collect data has improved greatly. It is an urgent problem to mine useful information or knowledge from large databases or data warehouses. Therefore, data mining technology is developed rapidly to meet the need. But DM (data mining) often faces so much data which is noisy, disorder and nonlinear. Fortunately, ANN (Artificial Neural Network) is suitable to solve the before-mentioned problems of DM because ANN has such merits as good robustness, adaptability, parallel-disposal, distributing-memory and high tolerating-error. This paper gives a detailed discussion about the application of ANN method used in DM based on the analysis of all kinds of data mining technology, and especially lays stress on the classification Data Mining based on RBF neural networks. Pattern classification is an important part of the RBF neural network application. Under on-line environment, the training dataset is variable, so the batch learning algorithm (e.g. OLS) which will generate plenty of unnecessary retraining has a lower efficiency. This paper deduces an incremental learning algorithm (ILA) from the gradient descend algorithm to improve the bottleneck. ILA can adaptively adjust parameters of RBF networks driven by minimizing the error cost, without any redundant retraining. Using the method proposed in this paper, an on-line classification system was constructed to resolve the IRIS classification problem. Experiment results show the algorithm has fast convergence rate and excellent on-line classification performance.

Paper Details

Date Published: 13 April 2009
PDF: 6 pages
Proc. SPIE 7344, Data Mining, Intrusion Detection, Information Security and Assurance, and Data Networks Security 2009, 73440N (13 April 2009); doi: 10.1117/12.817348
Show Author Affiliations
Lijuan Zhou, Capital Normal Univ. (China)
Harbin Institute of Technology (China)
Harbin Univ. of Science and Technology (China)
Min Xu, Renmin Univ. of China (China)
Capital Normal Univ. (China)
Zhang Zhang, Capital Normal Univ. (China)
Luping Duan, Harbin Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 7344:
Data Mining, Intrusion Detection, Information Security and Assurance, and Data Networks Security 2009
Belur V. Dasarathy, Editor(s)

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