Workload models of VBR video traffic and their use in resource allocation policies
IEEE/ACM Transactions on Networking (TON)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms
Statistical characteristics and multiplexing of MPEG streams
INFOCOM '95 Proceedings of the Fourteenth Annual Joint Conference of the IEEE Computer and Communication Societies (Vol. 2)-Volume - Volume 2
MPEG VBR video traffic modeling and classification using fuzzy technique
IEEE Transactions on Fuzzy Systems
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Centroid Neural Network With a Divergence Measure for GPDF Data Clustering
IEEE Transactions on Neural Networks
Classification of audio signals using Fuzzy c-Means with divergence-based Kernel
Pattern Recognition Letters
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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.