Clustering is to group similar objects into clusters. Until now there are a lot of approaches using Self-Organizing Feature Maps(SOFMs). But theyhave problems with a small output-layer nodes and initial weight. This paper suggests one-dimensional output-layer nodes in SOFMs. The number of output-layer nodes is more than those of clusters intended to find and the order of output-layer nodes is ascending in the sum of the output-layer node’s weight. We can find input data in SOFMs output node and classify input data in output nodes using the Euclidean Distance. The suggested algorithm was tested on well-known IRIS data and machine-part incidence matrix. The results of this computational study demonstrate the superiority of the suggested...
Abstract- The Self-Organizing Map (SOM) is an unsupervised neural network introduced in the 80’s by ...
Abstract – In this paper, we propose a new clustering method consisting in automated “flood- fill ...
A powerful method in the analysis of datasets where there are many natural clusters with varying sta...
Cluster analysis methods are used to classify R unlabeled objects in a P-dimensional space into grou...
Cluster analysis is the name given to a diverse collection of techniques that can be used to classif...
Abstract—The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It ...
This paper proposes a clustering ensemble method that introduces cascade structure into the self-org...
Data clustering is an important and widely used task of data mining that groups similar items togeth...
Abstract – The Self-Organizing Map (SOM) [1] is an effective tool for clustering and data mining. On...
Abstract. Clustering of data is one of the main applications of the Self-Organizing Map (SOM). U-mat...
Abstract. Clustering of data is one of the main applications of the Self-Organizing Map (SOM). U-mat...
Determining the structure of data without prior knowledge of the number of clusters or any informati...
Abstract — The Self-Organizing Map (SOM) is popular algo-rithm for unsupervised learning and visuali...
Abstract — The Self-Organizing Map (SOM) is popular algo-rithm for unsupervised learning and visuali...
Abstract – In this paper, we propose a new clustering method consisting in automated “flood- fill ...
Abstract- The Self-Organizing Map (SOM) is an unsupervised neural network introduced in the 80’s by ...
Abstract – In this paper, we propose a new clustering method consisting in automated “flood- fill ...
A powerful method in the analysis of datasets where there are many natural clusters with varying sta...
Cluster analysis methods are used to classify R unlabeled objects in a P-dimensional space into grou...
Cluster analysis is the name given to a diverse collection of techniques that can be used to classif...
Abstract—The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It ...
This paper proposes a clustering ensemble method that introduces cascade structure into the self-org...
Data clustering is an important and widely used task of data mining that groups similar items togeth...
Abstract – The Self-Organizing Map (SOM) [1] is an effective tool for clustering and data mining. On...
Abstract. Clustering of data is one of the main applications of the Self-Organizing Map (SOM). U-mat...
Abstract. Clustering of data is one of the main applications of the Self-Organizing Map (SOM). U-mat...
Determining the structure of data without prior knowledge of the number of clusters or any informati...
Abstract — The Self-Organizing Map (SOM) is popular algo-rithm for unsupervised learning and visuali...
Abstract — The Self-Organizing Map (SOM) is popular algo-rithm for unsupervised learning and visuali...
Abstract – In this paper, we propose a new clustering method consisting in automated “flood- fill ...
Abstract- The Self-Organizing Map (SOM) is an unsupervised neural network introduced in the 80’s by ...
Abstract – In this paper, we propose a new clustering method consisting in automated “flood- fill ...
A powerful method in the analysis of datasets where there are many natural clusters with varying sta...