Title :
Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering
Author :
Jaewook Lee ; Daewon Lee
Author_Institution :
Dept. of Ind. & Manage. Eng., Pohang Univ. of Sci. & Technol.
Abstract :
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
Keywords :
pattern clustering; support vector machines; cluster structures; constituent basin level cells; inductive support vector clustering; simplified weighted graph; Clustering algorithms; Clustering methods; Computational modeling; Kernel; Labeling; Machine learning; Robustness; Shape; Static VAr compensators; Support vector machines; Clustering; dynamical systems.; inductive learning; kernel methods; support vector machines; Algorithms; Artificial Intelligence; Cluster Analysis; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Pattern Recognition, Automated;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2006.225