Tran, Khoi-Nguyen; Jin, Huidong
Current data mining techniques have been developed with great success on homogeneous data. However, few techniques exist for heterogeneous data without further manipulation or consideration of dependencies among the different types of attributes. This paper presents a fusion of C4.5 Decision Tree and Gaussian Mixture Model (GMM) techniques for mixed-attribute data sets. The proposed fusion technique is used to detect anomalies in computer network data. Evaluation experiments were performed on...[Show more]
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