The fuzzy clustering algorithm is sensitive to the initial center and is easily convergence to local optimum. Inspired by the clone selection principle and memory mechanism of the vertebrate immune system, a new hybrid clustering method based on the modified artificial immune theory is presented. It not only adaptively determined the amount and the center's positions of clustering, but also avoided the local optima and the flaw about sensitive to the initialization. Also, the K-means algorithm is used as a search operator in order to improve the convergence speed. Experimental results indicate the validity and convergence of the proposed algorithm.