JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 4    Issue : 8    Date : August 2009

Subtractive Clustering Based RBF Neural Network Model for Outlier Detection
Peng Yang, Qingsheng Zhu, and Xun Zhong
Page(s): 755-762
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Abstract
Outlier detection has many important applications in the field of fraud detection, network robustness
analysis and intrusion detection. Some researches have utilized the neural network to solve the
problem because it has the advantage of powerful modeling ability. In this paper, we propose a RBF
neural network model using subtractive clustering algorithm for selecting the hidden node centers,
which can achieve faster training speed. In the meantime, the RBF network was trained with a
regularization term so as to minimize the variances of the nodes in the hidden layer and perform
more accurate prediction. By defining the degree of outlier, we can effectively find the abnormal data
whose actual output is serious deviation from its expectation as long as the output is certainty.
Experimental results on different datasets show that the proposed RBF model has higher detection
rate as well as lower false positive rate comparing with the other methods, and it can be an effective
solution for detecting outliers.

Index Terms
outlier detection, radial basis function, neural network, subtractive clustering