Density based spatial clustering of applications with noise （DBSCAN） has poor scalability on the data size, especially when the amount of data increases. Here an improved adaptive fast density based spatial clustering of applications with noise (F DBSCAN) algorithm is proposed, with no longer checks of the objects inside the neighborhood of core obj ects, but just the mark of them. Merging clusters is performed by determining whether th ere exist the marked objects in the neighborhood of core objects. Noisy objects are recognized by checking whether the neighborhood of border ones contains a core ones. The proposed algorithm can avoid the repeated checking of overlapping are a of the original DBSCAN without building the spatial index, thus improving its eff iciency substantially with time complexity approaching O(nlogn). The clustering quality of FDBSCAN is validated on both artificial and real datasets, and its efficiency is also validated on two real datasets from different industries. The empirical results suggest that FDBSCAN can achieve good clustering qualit y as well as better efficiency and scalability.