A grid dynamic clustering algorithm based on density

Clustering algorithm is an important research direction in data-mining field.So far people have presented many clustering algorithms which applied to large-scale or high-dimension databases.Clustering algorithm based on density is one of the typical research directions.Based on DBSCAN,this paper presents GDCABD(a grid dynamic clustering algorithm based on density).The new algorithm puts the theory of grid into clustering algorithm which based on density.It also adopts dynamic parameter method,thus can automatically do necessary parameter modify according to data distribution,meanwhile reduce the sensitivity of DBSCAN to original parameters.As a result,it improves the efficiency and effect of clustering,at the same time reduces the cost of I/O.The algorithm can not only mine various-shape clustering,but also accurately mine prominent clustering in data sets.