A fast clustering algorithm based on grid and density condensation point
Zhuo Chen, Wei Zhen-gang · Ha'erbin gongye daxue xuebao · 2005
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.