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Cluster validation techniques are essential tools within cluster analysis, helpful to the interpretation of clustering results. In this study, the validation ability of Dunn's index in gene clustering was investigated with public gene expression datasets clustered by hierarchical clustering, K-means and Self-organizing maps. It was made clear that Dunn's index would give misleading validity results...
The Kohonen self organizing map (SOM) is an excellent tool in exploratory phase of data mining. The SOM is a popular tool that maps a high-dimensional space onto a small number of dimensions by placing similar elements close together, forming clusters. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units needs to be grouped i.e., clustered...
The Kohonen self organizing map is widely used as a popular tool in the exploratory phase of data mining. The SOM (self organizing maps) maps high dimensional space into a 2-dimensional grid by placing similar elements close together, forming clusters. Recently research experiments presented that to capture the uncertainty involved in cluster analysis, it is not necessary to have crisp boundaries...
Self-organizing map (SOM) has been recognized as a powerful tool in cluster analysis. This paper presents a fuzzy SOM algorithm for mixed numeric and categorical data which integrates fuzzy set theory in model exploration through a fuzzy projection instead of crisp projection. In addition, a hybrid clustering approach is proposed combining SOMs with partitive clustering algorithms for the sake of...
Clustering is the process of discovering groups within multidimensional data, based on similarities, with a minimal, if any, knowledge of their structure. Distributed data clustering is a recent approach to deal with geographically distributed databases, since traditional clustering methods require centering all databases in a single dataset. Moreover, current privacy requirements in distributed databases...
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