The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Détecter les clusters naturels pour une distribution de données de forme irrégulière est une tâche difficile en reconnaissance de formes. Dans cette étude, nous proposons un algorithme de clustering efficace pour les clusters de forme irrégulière, basé sur les avantages du clustering spectral et de l'algorithme de propagation d'affinité (AP). Nous donnons une nouvelle mesure de similarité basée sur une analyse de dispersion de voisinage. L'algorithme proposé est une méthode simple mais efficace. Les résultats expérimentaux sur plusieurs ensembles de données montrent que l'algorithme peut détecter les groupes naturels d'ensembles de données d'entrée et que les résultats de regroupement concordent bien avec ceux du jugement humain.
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.
Copier
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
Copier
@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},}
Copier
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 -