This paper presents our work on improving an existing density-based clustering algorithm. By using Bayesian sequential partitioning (BSP) in the density estimation part of the algorithm, we were able to significantly reduce the computational complexity of the clustering algorithm. Simulation results showed 15 to 40% reduction in computation time, depending on the dimensions of the data, while retaining the clustering accuracy of the original algorithm.