Extended Kernel Self-Organizing Map Clustering Algorithm

The self-organizing map allows to visualize the underlying structure of high dimensional data. However, the original relies on the use of Euclidean distances which often becomes a serious drawback for number of real problems. Donald and others map the data in input space into a high 2-dimension feature space, here SOM algorithm are performed. However, its disadvantage lies in lack of direct descriptions about the clustering´s center and result .In this paper, we extend of SOM, a novel kernel SOM algorithm is proposed from energy function. The idea of kernel self-organizing map is applied to kernel trick. The inner product of the mapping value of the original data in feature space is replaced by a kernel function, the winner neuron and weights of each neuron can be initialized and updated by kernel Euclidean norm in the feature space. This trick resolve the non-liners can´t clustering in the input space and can´t direct descriptions about the clustering´s center and result. In this paper, some data are applied to test KSOM and SOM algorithm,The result of the experiments show KSOM algorithm has better performance than SOM.