A Dispersive Degree based Clustering Algorithm Combined with Classification
- https://doi.org/10.2991/iske.2007.180How to use a DOI?
- Clustering analysis; Various-density; Dispersive degree; Data mining
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.
- © 2007, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xianchao Zhang AU - Shimin Shan AU - Zhihang Yu AU - He Jiang PY - 2007/10 DA - 2007/10 TI - A Dispersive Degree based Clustering Algorithm Combined with Classification BT - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007) PB - Atlantis Press SP - 1059 EP - 1065 SN - 1951-6851 UR - https://doi.org/10.2991/iske.2007.180 DO - https://doi.org/10.2991/iske.2007.180 ID - Zhang2007/10 ER -