MPEG Video Traffic Modeling and Classification Using Fuzzy C-Means Algorithm with Divergence-Based Kernel
A modeling and classification model for MPEG video traffic data using a Fuzzy C-Means algorithm with a Divergence-based Kernel (FCMDK) for clustering GPDF data is proposed in this paper. The FCMDK is based on the Fuzzy C-Means clustering algorithm and thus exploits advantageous features of fuzzy clustering techniques. To further improve classification accuracies and deal with nonlinear data, the input data is projected into a feature space of a higher dimensionality. Consequently, nonlinear problems existing in the input space can be solved linearly in the feature space. The divergence-based kernel method adopted in the FCMDK employs a divergence measure between two probability distributions for its similarity measure. By adopting the divergence-based kernel method for probability data, the FCMDK can not only utilize advantageous features of the kernel method but also exploit the statistical nature of the input data. Experiments and results on several MPEG video traffic data sets demonstrate that the classification model employing the FCMDK for clustering GPDF data can archive improvements of 28.19% and 34.60% in terms of False Alarm Rate (FAR) over the models using the conventional k-means and SOM algorithms, respectively.
KeywordsFeature Space False Alarm Rate Gaussian Mixture Model Membership Grade Code Vector
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