The negative selection algorithm (NSA) is one of the basic algorithms of the artificial immune system. In the traditional negative selection algorithm, candidate detectors are randomly generated without considering the uneven distributions of self-antigens and nonself-antigens, thereby resulting in many redundant detectors, and it is difficult for these detectors to fully cover the area of nonself-antigens. To overcome the problem of low detector generation efficiency, a negative selection algorithm that is based on antigen density clustering (ADC-NSA) is proposed in this paper. The algorithm divides the process of detector generation into three steps: the first step is to calculate the density of the antigens by using the method of antigen density clustering to select nonself-clusters. The second step is to prioritize the abnormal points (nonself-antigens that are not clustered) as the centers of candidate detectors and to generate the detectors via calculation. The third step is to generate the detectors via the traditional algorithm. Detector generation via these three steps can reduce the randomness of the detector generation in the traditional algorithm, thereby improving the efficiency of detector generation. The experimental results demonstrate that on the BCW and KDD-Cup datasets, the negative selection algorithm that is based on antigen density clustering can effectively increase the detection rate while reducing the false-positive rate compared with the traditional negative selection algorithm (RNSA) and two improved algorithms at the same expected coverage.