A new incremental clustering algorithm is proposed in this paper based on the relativity principle, which means that the similarities of objects in the same cluster is higher than those among different clusters. This approach not only inherits the advantages of absolute density based algorithms which can discover arbitrary shape clusters and are insensitive to noises^［1］, but also efficiently solves the following common problems: clustering results are very sensitive to the user-defined parameters, reasonable parameters are hard to be determined, and high density clusters are contained fully in coterminous low density clusters. With this approach, incremental clustering can also be supported effectively by defining the affected sets and seed sets of the updating objects in this approach.