Data mining based on Web logs is of great significance. For instance, it can discover groups of people with similar interests and facilitate recommendation and personal service. A new clustering method based on Web users' interests regressively analyzes users' behaviors, partitions the interesting matrix with a threshold λ, and finally relocates some elements of clusters based on the joint strength between an element and a cluster. The favorable precision and scalability of the algorithm are studied through the experiments.