An Efficient Clustering Algorithm for Irregularly Shaped Clusters
Summary :
To detect the natural clusters for irregularly shaped data distribution is a difficult task in pattern recognition. In this study, we propose an efficient clustering algorithm for irregularly shaped clusters based on the advantages of spectral clustering and Affinity Propagation (AP) algorithm. We give a new similarity measure based on neighborhood dispersion analysis. The proposed algorithm is a simple but effective method. The experimental results on several data sets show that the algorithm can detect the natural clusters of input data sets, and the clustering results agree well with that of human judgment.
- Publication
- IEICE TRANSACTIONS on Information Vol.E93-D No.2 pp.384-387
- Publication Date
- 2010/02/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E93.D.384
- Type of Manuscript
- LETTER
- Category
- Pattern Recognition
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
DongMing TANG, QingXin ZHU, Yong CAO, Fan YANG, "An Efficient Clustering Algorithm for Irregularly Shaped Clusters" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 2, pp. 384-387, February 2010, doi: 10.1587/transinf.E93.D.384.
Abstract: To detect the natural clusters for irregularly shaped data distribution is a difficult task in pattern recognition. In this study, we propose an efficient clustering algorithm for irregularly shaped clusters based on the advantages of spectral clustering and Affinity Propagation (AP) algorithm. We give a new similarity measure based on neighborhood dispersion analysis. The proposed algorithm is a simple but effective method. The experimental results on several data sets show that the algorithm can detect the natural clusters of input data sets, and the clustering results agree well with that of human judgment.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.384/_p
Copy
@ARTICLE{e93-d_2_384,
author={DongMing TANG, QingXin ZHU, Yong CAO, Fan YANG, },
journal={IEICE TRANSACTIONS on Information},
title={An Efficient Clustering Algorithm for Irregularly Shaped Clusters},
year={2010},
volume={E93-D},
number={2},
pages={384-387},
abstract={To detect the natural clusters for irregularly shaped data distribution is a difficult task in pattern recognition. In this study, we propose an efficient clustering algorithm for irregularly shaped clusters based on the advantages of spectral clustering and Affinity Propagation (AP) algorithm. We give a new similarity measure based on neighborhood dispersion analysis. The proposed algorithm is a simple but effective method. The experimental results on several data sets show that the algorithm can detect the natural clusters of input data sets, and the clustering results agree well with that of human judgment.},
keywords={},
doi={10.1587/transinf.E93.D.384},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - An Efficient Clustering Algorithm for Irregularly Shaped Clusters
T2 - IEICE TRANSACTIONS on Information
SP - 384
EP - 387
AU - DongMing TANG
AU - QingXin ZHU
AU - Yong CAO
AU - Fan YANG
PY - 2010
DO - 10.1587/transinf.E93.D.384
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2010
AB - To detect the natural clusters for irregularly shaped data distribution is a difficult task in pattern recognition. In this study, we propose an efficient clustering algorithm for irregularly shaped clusters based on the advantages of spectral clustering and Affinity Propagation (AP) algorithm. We give a new similarity measure based on neighborhood dispersion analysis. The proposed algorithm is a simple but effective method. The experimental results on several data sets show that the algorithm can detect the natural clusters of input data sets, and the clustering results agree well with that of human judgment.
ER -
English
