An Automatic Kernel of Graph Clustering Method in Conforming Clustering Number

Based on analyzing graph theory knowledge and kernel function theory, every data sample is considered as top point V in graph, so all data samples consist of nondirectional weighted graph G = (V,E) , which takes similarity as weighted value. In the perspective of graph theory, this article defines connected modulus, which can fully reflect the best clustering number. This modulus categorizes similar text into a connected graph, and keeps the clearance of physical meaning. In this paper, a Kernel of Graph Clustering method based on clustering was proposed, this arithmetic is compared with kernel C-equal value arithmetic. The test justifies that this arithmetic not only has less complexness in time and space, but also good robustness.