Kernel Density Peaks

We develop the clustering approach by fast-search-and-find of density peaks which use the kernel function for mapping the input sample objects to the high dimensional feature space and amplifying the original sample objects’ characteristics, thereby researching the local density of the object attribute based on similarity measure. In order to identify our method’s effectiveness, we compare our method to other popular clustering algorithms. From the result analysis, we could conclude that our method have a better result.