This paper proposes a simple but efficient density-based clustering, named CBSCAN, to fast discover cluster patterns with arbitrary shapes and noises from location big data effectively. Firstly, the notion of Cell is defined and a distance analysis principle based on Cell is proposed to quickly find core points in high density areas and density relationships with other points without distance computing. Secondly, a Cell-based cluster that maps point-based density cluster to grid-based density cluster is presented. By leveraging exclusion grids and relationships with their adjacent grids, all inclusion grids of Cell-based cluster can be rapidly determined. Furthermore, a fast density-based algorithm based on the distance analysis principle and Cell-base cluster is implemented to transform DBSCAN of point-based expansion to Cell-based expansion clustering. The proposed algorithm improves clustering efficiency significantly by using inherent property of location data to reduce huge number of distance calculations. Finally, comprehensive experiments on benchmark datasets demonstrate the clustering effectiveness of the proposed algorithm. Experimental results on massive-scale real and synthetic location datasets show that CBSCAN improves 525 fold, 30 fold and 11 fold of efficiency compared with DBSCAN, DBSCAN with PR-Tree and Grid index optimization respectively.