Self-adapted clustering algorithm based on grid density
一种基于网格密度的自适应聚类分析算法*

This paper presented a new efficient clustering algorithm that combined the approach based on density and grid. The most creativity of this novel algorithm was capturing the shape and extent of a cluster by using grid, and then analyzed the data based on the grid density. It also could reach high efficiency because of its linear time complexity. Both theory analysis and experimental results prove that this algorithm can discover clusters with arbitrary shape and is insensitive to noise data.