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论文类型： 会议论文

发表时间： 2007-01-01

收录刊物： CPCI-S

关键字： clustering analysis; various-density; dispersive degree; data mining

摘要： 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 were 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.