  DBSCAN is a very classic algorithm for data clus- tering, which is widely
used in many fields. However, with the data scale growing much more bigger than
before, the traditional serial algorithm can not meet the performance
requirement. Recently, parallel computing based on CUDA has developed very fast
and has great advantage on big data. This paper summarizes the algorithms
proposed before and improves the performance of the old DBSCAN algorithm by
using CUDA and parallel computing. The algorithm uses shared memory as much as
possible compared with other algorithms and it has very good scalability. A
data set is tested on the new version of DBSCAN. Finally, we analyze the
results and give a conclusion that our algorithm is approximately 97 times
faster than the serial version.
