A Clustering Algorithm Based on Density Kernel Extension

Abstract

A new type of clustering algorithm called CADEKE is presented in this paper. CADEKE creates an extended density kernel structure for every cluster by using its neighborhood coefficient. Those unprocessed objects found in current kernel structure are added to extend the kernel structure until no new object is found. Each density kernel structure is regarded as one cluster. CADEKE requires only one input parameter as the initial radius of finding the density kernel and has no limitation on density threshold. Other characteristics include the capacity of discovering clusters with arbitrary shapes and processing the noise data. The results of our experiments demonstrate that CADEKE is significantly more accurate in discovering density-changeable clustering than the algorithm DBSCAN, and that CADEKE is less sensitive to input parameters.