Although many clustering algorithms have been proposed so far, seldom was focused on high-dimensional and incremental databases. This paper introduces a grid density-based clustering algorithm GDCA. which discovers clusters with arbitrary shape in spatial databases. It first partitions the data space into a number of units, and then deals with units instead of points. Only those units with the density no less than a given minimum density threshold are useful in extending clusters. An incremental clustering algorithm----IGDCA is also presented, applicable in periodically incremental environment.