The scale of spatial data is usually very large. Clustering algorithm needs very high performance, good scalability, and able to deal with noise data and high-dimensional data. Proposed a quickly clustering algorithm based on one-dimensional distance calculation. The algorithm first partitions space-sets by one-dimensional distance, then clusters space-sets by set-distance and set-density. Next, uses the same approach to the next dimension, until all dimensions have been processed. Experimental results show ODDC algorithm has high-efficient features and is not sensitive to noise data.