Add to ORKG”We are drowning in information, but starving for knowledge. ” [John Naisbett] The objective of exploratory data analysis is to produce simplified descriptions and summaries of large data sets. Clustering: Discover similarity relations between data objects in high-dimensional signal space. Self Organizing Maps: Project high-dimensional signal space on a two-dimensional grid of nodes while preserving the topological relationships of the signal space on the two-dimensional display
Cluster analysis divides data into groups (clusters) for the purposes of summarization or improved u...
Abstract—The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It ...
Cluster analysis is the name given to a diverse collection of techniques that can be used to classif...
: We describe an extension to the self-organizing map learning rule enabeling a straight-forward vis...
. Iterative, EM-type algorithms for data clustering and data visualization are derived on the basis ...
Clustering has been applied in many areas, including signal and image processing. This chapter revie...
For using Data Mining, especially cluster analysis, one needs measures to determine the similarity o...
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. However, d...
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...
Dimensionality reduction and clustering techniques are frequently used to analyze complex data sets,...
Cluster analysis of multidimensional data is widely used in many research areas including financial,...
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...
Research on the problem of clustering tends to be fragmented across the pattern recognition, databas...
Cluster analysis divides data into groups (clusters) for the purposes of summarization or improved u...
Abstract—The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It ...
Cluster analysis is the name given to a diverse collection of techniques that can be used to classif...
: We describe an extension to the self-organizing map learning rule enabeling a straight-forward vis...
. Iterative, EM-type algorithms for data clustering and data visualization are derived on the basis ...
Clustering has been applied in many areas, including signal and image processing. This chapter revie...
For using Data Mining, especially cluster analysis, one needs measures to determine the similarity o...
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. However, d...
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...
Dimensionality reduction and clustering techniques are frequently used to analyze complex data sets,...
Cluster analysis of multidimensional data is widely used in many research areas including financial,...
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...
Research on the problem of clustering tends to be fragmented across the pattern recognition, databas...
Cluster analysis divides data into groups (clusters) for the purposes of summarization or improved u...
Abstract—The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It ...
Cluster analysis is the name given to a diverse collection of techniques that can be used to classif...