An automatic clustering method based on distance evaluation function

In spatial clustering, the key factor to solve the problem of optimal class number is to construct a proper cluster validity function. The value of k must be confirmed in advance to exert K-means algorithm. However, it can not be clearly and easily confirmed in fact for its uncertainty. This paper recommends a distance evaluation function based on Euclidean distance to confirm the optimal class number, designs a new optimization algorithm of k value. The experiential rule which is usually expressed as k
max
n is theoretically proved to be reasonable. Results come from the example also show the validity of this new algorithm.