Abstract: Inspired by the immune response principle, an artificial immune system framework for incremental data clustering is proposed. Meanwhile, a novel self organizing incremental clustering algorithm called IRA (Immune response algorithm) is also proposed based on the framework. IRA uses Logistic chaotic sequence to produce the initial antibody population. The diversity of the chaotic sequence is used for recognizing the incremental data which do not belong to any existing clusters. This process simulates the primary immune response. At the same time, the memory antibodies produced by the primary immune response are used for the secondary immune response, that is, they can recognize the incremental data which belong to the existing cluster. In order to reduce the data redundancy, the algorithm uses the center and representative points to represent the existing clusters. The recognizing scopes of them are updated dynamically. Therefore, the algorithm not only can form clusters dynamically and self organization, but also can achieve data feature selection. The experimental results show that the algorithm has advantages on both the clustering quality and the data feature selection. Furthermore, it has some other merits, such as few parameters, fast speed, insensitivity to input and so on. So the algorithm has some value for practical problems.