一种基于网格和密度凝聚点的快速聚类算法

A fast clustering algorithm based on grid and density condensation point

提出的快速聚类算法通过凝聚点来准确反映数据空间的几何特征,然后采用网格和密度相结合的方法,利用爬山法和连通性原理进行聚类处理,克服了传统网格聚类算法聚类质量降低的缺点.实验结果证明,本算法的聚类效率优于传统爬山法、C lique算法和DBSCAN算法.

A new kind of clustering algorithm called CGDCP is presented. The creativity of CGDCP is capturing the shape of data space by condensation points, and then using grid - based and density - based clustering methods based on the theories of a climbing hill algorithm and connectivity to deal with the data. CGDCP retains the good features of grid - based and density - based clustering methods and overcomes the traditional shortcomings of the grid - based clustering method＇s quality debasement resulting from little or no consideration of data distribution when partitioning the grids. Experimental results confirm that the execution efficiency of CGDCP is much better than a traditional climbing hill algorithm and the Clique algorithm.