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A topological and dynamical characterization of the cluster structures described by the support vector clustering is developed. It is shown that each cluster can be decomposed into its constituent basin level cells and can be naturally extended to an enlarged clustered domain, which serves as a basis for inductive clustering. A simplified weighted graph preserving the topological structure of the clusters is also constructed and is employed to develop a robust and inductive clustering algorithm. Simulation results are given to illustrate the robustness and effectiveness of the proposed method
Robustness, Kernel, Clustering algorithms, Static VAr compensators, Support vector machines, Labeling, Machine learning, Shape, Computational modeling, Clustering methods
"Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 1869-1874, November 2006, doi:10.1109/TPAMI.2006.225
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