A Dispersive Degree based Clustering Algorithm Combined with Classification

DOI

10.2991/iske.2007.180How to use a DOI?

Keywords

Clustering analysis; Various-density; Dispersive degree; Data mining

Abstract

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.

Copyright

© 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/).

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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  - 10.2991/iske.2007.180
ID  - Zhang2007/10
ER  -