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The various-density problem has become one of the focuses in density based clustering research. A novel dispersive degree based algorithm combined with classification, called CDDC, is presented in this paper to remove the hurdle. In CDDC, a sequence is established for depicting the data distribution, discriminating cores and classifying edges. Clusters are discovered by utilizing the revealed information. Several experiments are performed and the results suggest that CDDC is effective in handling the various-density problem and is more efficient than the well-known algorithms such as DBSCAN, OPTICS and KNNCLUST.

A Dispersive Degree based Clustering Algorithm Combined with Classification