Abstract: In order to solve the problem that the density clustering algorithm is sensitive to neighborhood parameters, this article introduces a density-based fuzzy adaptive clustering algorithm. Without predefined clustering number and neighborhood parameters, this algorithm adaptively determines the radius of neighborhood to obtain the density of each sample and increases cluster centers based on the density. A new validity measure for fuzzy clustering is proposed to choose the best clustering number so that the sensitivity of density clustering is eliminated. UCI benchmark data sets are used to compare the proposed algorithm and the traditional density clustering algorithm. Experiment results demonstrate that the proposed algorithm improves the clustering accuracy and the adaptability effectively.