Abstract: In order to find space information and knowledge in sample points: when clustering point replaces class-mean clustering, the quantized value that describes index contribution to clustering is abstracted, then index classification weight is defined. By using classification weight, weighted distance between sample point and clustering point is defined. As similarity measurement between sample point and class, this distance is more reasonable. Transform weighted distance into sample membership. In order to avoid randomicity caused by sequential algorithm, the mass center of the sample point set is utilized to modify the present clustering points of K classes and the sample points use K memberships as their masses. From this, an iterative algorithm based on classification weight and mass center driving for searching clustering points is proposed. IRIS is used to verify this algorithm and the result shows that clustering effect and stability are superior to the existing unsupervised learning algorithms.