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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Detectar os clusters naturais para distribuição de dados de formato irregular é uma tarefa difícil no reconhecimento de padrões. Neste estudo, propomos um algoritmo de agrupamento eficiente para clusters de formato irregular baseado nas vantagens do agrupamento espectral e do algoritmo de propagação de afinidade (AP). Fornecemos uma nova medida de similaridade baseada na análise de dispersão de vizinhança. O algoritmo proposto é um método simples, mas eficaz. Os resultados experimentais em vários conjuntos de dados mostram que o algoritmo pode detectar os agrupamentos naturais dos conjuntos de dados de entrada, e os resultados do agrupamento concordam bem com o julgamento humano.
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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
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@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},}
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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 -