Abstract: A clustering result evaluating algorithm is presented in a gravitational way, where all the data points in the data space are regarded as the particles assigned with unit mass. The quality of such a clustering result is evaluated through analyzing the gravitational relation between different data points in the clustering result in which the greater the gravitation between data points, the smaller the gravitation acted on noise data points-this is regarded as a quality result and vice versa. Experiments conducted on several datasets verify the validity and high efficiency of the proposed algorithm which can get an evaluation value to reflect whether the clustering result is of good or poor quality. Furthermore, the proposed algorithm can lead the clustering algorithm to find the best result automatically without any manual interference.