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
Self-Organizing Map (SOM) est un outil puissant pour l’exploration des méthodes de clustering. Le clustering est la tâche la plus importante dans l'apprentissage non supervisé et la validité du clustering est un problème majeur dans l'analyse cluster. Dans cet article, un nouvel indice de validité de clustering est proposé pour générer le résultat de clustering d'un SOM à deux niveaux. Ceci est effectué en utilisant le taux de séparation inter-cluster, la densité relative de l’inter-cluster et le taux de cohésion intra-cluster. L'indice de validité du clustering est proposé pour trouver le nombre optimal de clusters et déterminer quels deux clusters voisins peuvent être fusionnés dans un clustering hiérarchique d'un SOM à deux niveaux. Les expériences montrent que l'algorithme proposé est capable de regrouper les données avec plus de précision que les algorithmes de clustering classiques basés sur un SOM à deux niveaux et qu'il est mieux à même de trouver un nombre optimal de clusters en maximisant l'indice de validité du clustering.
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Shu-Ling SHIEH, I-En LIAO, "A New Clustering Validity Index for Cluster Analysis Based on a Two-Level SOM" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 9, pp. 1668-1674, September 2009, doi: 10.1587/transinf.E92.D.1668.
Abstract: Self-Organizing Map (SOM) is a powerful tool for the exploratory of clustering methods. Clustering is the most important task in unsupervised learning and clustering validity is a major issue in cluster analysis. In this paper, a new clustering validity index is proposed to generate the clustering result of a two-level SOM. This is performed by using the separation rate of inter-cluster, the relative density of inter-cluster, and the cohesion rate of intra-cluster. The clustering validity index is proposed to find the optimal numbers of clusters and determine which two neighboring clusters can be merged in a hierarchical clustering of a two-level SOM. Experiments show that, the proposed algorithm is able to cluster data more accurately than the classical clustering algorithms which is based on a two-level SOM and is better able to find an optimal number of clusters by maximizing the clustering validity index.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1668/_p
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@ARTICLE{e92-d_9_1668,
author={Shu-Ling SHIEH, I-En LIAO, },
journal={IEICE TRANSACTIONS on Information},
title={A New Clustering Validity Index for Cluster Analysis Based on a Two-Level SOM},
year={2009},
volume={E92-D},
number={9},
pages={1668-1674},
abstract={Self-Organizing Map (SOM) is a powerful tool for the exploratory of clustering methods. Clustering is the most important task in unsupervised learning and clustering validity is a major issue in cluster analysis. In this paper, a new clustering validity index is proposed to generate the clustering result of a two-level SOM. This is performed by using the separation rate of inter-cluster, the relative density of inter-cluster, and the cohesion rate of intra-cluster. The clustering validity index is proposed to find the optimal numbers of clusters and determine which two neighboring clusters can be merged in a hierarchical clustering of a two-level SOM. Experiments show that, the proposed algorithm is able to cluster data more accurately than the classical clustering algorithms which is based on a two-level SOM and is better able to find an optimal number of clusters by maximizing the clustering validity index.},
keywords={},
doi={10.1587/transinf.E92.D.1668},
ISSN={1745-1361},
month={September},}
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TY - JOUR
TI - A New Clustering Validity Index for Cluster Analysis Based on a Two-Level SOM
T2 - IEICE TRANSACTIONS on Information
SP - 1668
EP - 1674
AU - Shu-Ling SHIEH
AU - I-En LIAO
PY - 2009
DO - 10.1587/transinf.E92.D.1668
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2009
AB - Self-Organizing Map (SOM) is a powerful tool for the exploratory of clustering methods. Clustering is the most important task in unsupervised learning and clustering validity is a major issue in cluster analysis. In this paper, a new clustering validity index is proposed to generate the clustering result of a two-level SOM. This is performed by using the separation rate of inter-cluster, the relative density of inter-cluster, and the cohesion rate of intra-cluster. The clustering validity index is proposed to find the optimal numbers of clusters and determine which two neighboring clusters can be merged in a hierarchical clustering of a two-level SOM. Experiments show that, the proposed algorithm is able to cluster data more accurately than the classical clustering algorithms which is based on a two-level SOM and is better able to find an optimal number of clusters by maximizing the clustering validity index.
ER -