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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.
Clustering, kernel methods, support vector machines, inductive learning, dynamical systems.

D. Lee and J. Lee, "Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 1869-1874, 2006.
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