出版社：Japan Society for Fuzzy Theory and Intelligent Informatics
摘要：This paper aims at discussing a clustering algorithm based on Learning Vector Quantization (LVQ) using a kernel function in support vector machines. Mapping object data into the high-dimensional feature space, this algorithm can find nonlinear boundaries between clusters which ordinary algorithms cannot find. The reason why kernel-based algorithms can find nonlinear clusters is that they may be linearly separated in the high-dimensional feature space. Nevertheless, actual configuration of data units in the high-dimensional feature space is unknown. Self-Organizing Map (SOM) associated with LVQ is hence applied with a kernel function. The resulting topological map for data in the high-dimensional feature space can visualize linearly separated clusters found by the proposed method. Numerical examples are given to show effectiveness of the proposed method when compared with fuzzy c-means and kernel-based fuzzy c-means.
关键词：SOM ; LVQ ; Clustering ; Kernel function ; Support vector machine
