A cluster analysis method is proposed in this paper. As benchmark data, the Fisher's iris and the Wine recognition data sets are used. As a result of the numerical experiment, a clustering method using the dendrogram yielded 97 % in accuracy. It is difficult to display a multi-dimensional data by the dendrogram in one dimension. The ultimate visualization is by means of 3 dimensional rendition. We conclude that the best way that a multi-dimensional data set is visualized is by a sphere, since the phase relationship of it is smooth everywhere
Clustering is an important technique for understanding and analysis of large multi-dimensional datas...
Data clustering is an important data exploration technique with many applications in data mining. Th...
Determining the structure of data without prior knowledge of the number of clusters or any informati...
A cluster analysis method is proposed in this paper. As benchmark data, the Fisher's iris and the Wi...
This work introduces an alternative representation for large dimensional data sets. Instead of using...
Cluster analysis is the name given to a diverse collection of techniques that can be used to classif...
Cluster analysis methods are used to classify R unlabeled objects in a P-dimensional space into grou...
Abstract –A new clustering algorithm based on emergent SOM is proposed. This algorithm, called U*C, ...
This thesis introduces n-sphere clustering, a new method of cluster analysis, akin to agglomerative ...
The Self-Organizing Map (SOM) algorithm is a popular and widely used cluster algorithm. Its constrai...
The Self-Organizing Map (SOM) algorithm is a popular and widely used cluster algorithm. Its constrai...
Abstract — In this paper, we re-consider the problem of mapping a high-dimensional data set into a l...
High-dimensional data is increasingly becoming common because of its rich information content that c...
The Self-Organizing Map (SOM) is one of the artificial neural networks that perform vector quantizat...
Abstract: This paper describes an approach to pixel clustering using self-organising map (SOM) techn...
Clustering is an important technique for understanding and analysis of large multi-dimensional datas...
Data clustering is an important data exploration technique with many applications in data mining. Th...
Determining the structure of data without prior knowledge of the number of clusters or any informati...
A cluster analysis method is proposed in this paper. As benchmark data, the Fisher's iris and the Wi...
This work introduces an alternative representation for large dimensional data sets. Instead of using...
Cluster analysis is the name given to a diverse collection of techniques that can be used to classif...
Cluster analysis methods are used to classify R unlabeled objects in a P-dimensional space into grou...
Abstract –A new clustering algorithm based on emergent SOM is proposed. This algorithm, called U*C, ...
This thesis introduces n-sphere clustering, a new method of cluster analysis, akin to agglomerative ...
The Self-Organizing Map (SOM) algorithm is a popular and widely used cluster algorithm. Its constrai...
The Self-Organizing Map (SOM) algorithm is a popular and widely used cluster algorithm. Its constrai...
Abstract — In this paper, we re-consider the problem of mapping a high-dimensional data set into a l...
High-dimensional data is increasingly becoming common because of its rich information content that c...
The Self-Organizing Map (SOM) is one of the artificial neural networks that perform vector quantizat...
Abstract: This paper describes an approach to pixel clustering using self-organising map (SOM) techn...
Clustering is an important technique for understanding and analysis of large multi-dimensional datas...
Data clustering is an important data exploration technique with many applications in data mining. Th...
Determining the structure of data without prior knowledge of the number of clusters or any informati...