Add to ORKGAn histogram data is described by a set of distributions. In this paper, we propose a clustering approach using an adaptation of the Self-Organizing Map (SOM) algorithm. The idea is to combine the dimension reduction obtained with a SOM and the clustering of the data in this reduced space. The L2 Wasserstein distance is used to measure dissimilarity between distributions and to estimate local data densities in the original space. The main advantage of the proposed algorithm is that the number of clusters is found automatically. Applications on synthetic and real data sets demonstrate the validity of the proposed approach
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capa...
Self-organizing maps are powerful for cluster extraction dueto their ability of obtaining a topologi...
Dimensional reduction is a widely used technique for exploratory analysis of large volume of data....
An histogram data is described by a set of distributions. In this paper, we propose a clustering ap...
Determining the structure of data without prior knowledge of the number of clusters or any informati...
Cluster analysis methods are used to classify R unlabeled objects in a P-dimensional space into grou...
A powerful method in the analysis of datasets where there are many natural clusters with varying sta...
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. However, d...
This paper presents a Dynamic Clustering Algorithm for histogram data with an automatic weighting st...
Abstract – The Self-Organizing Map (SOM) [1] is an effective tool for clustering and data mining. On...
Abstract –A new clustering algorithm based on emergent SOM is proposed. This algorithm, called U*C, ...
Abstract –A new clustering algorithm based on emergent SOM is proposed. This algorithm, called U*C, ...
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capa...
High-dimensional data is increasingly becoming common because of its rich information content that c...
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capa...
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capa...
Self-organizing maps are powerful for cluster extraction dueto their ability of obtaining a topologi...
Dimensional reduction is a widely used technique for exploratory analysis of large volume of data....
An histogram data is described by a set of distributions. In this paper, we propose a clustering ap...
Determining the structure of data without prior knowledge of the number of clusters or any informati...
Cluster analysis methods are used to classify R unlabeled objects in a P-dimensional space into grou...
A powerful method in the analysis of datasets where there are many natural clusters with varying sta...
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. However, d...
This paper presents a Dynamic Clustering Algorithm for histogram data with an automatic weighting st...
Abstract – The Self-Organizing Map (SOM) [1] is an effective tool for clustering and data mining. On...
Abstract –A new clustering algorithm based on emergent SOM is proposed. This algorithm, called U*C, ...
Abstract –A new clustering algorithm based on emergent SOM is proposed. This algorithm, called U*C, ...
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capa...
High-dimensional data is increasingly becoming common because of its rich information content that c...
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capa...
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capa...
Self-organizing maps are powerful for cluster extraction dueto their ability of obtaining a topologi...
Dimensional reduction is a widely used technique for exploratory analysis of large volume of data....