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Cluster Connections : A visualizationtechnique to reveal cluster boundariesin self-organizing
This paper suggests an extension to the standard map representation that leads to an easy recognition of cluster boundaries and allows intuitive analysis of the similarities inherent in the input data without the necessity of substantial prior knowledge, and an intuitive recognition of clusters boundaries.
Alternative Ways for Cluster Visualization inSelf-Organizing
Two enhanced visualization techniques for the self-organizing map allowing the intuitive representation of input data similarity are presented and can be combined to allow improved analysis of the inherent structure of high-dimensional input data and an intuitive recognition of cluster boundaries without the necessity of substantial prior knowledge concerning the input patterns.
Coverage Criteria for Testing DMM Specications
Uncovering Associations Between DocumentsDieter Merkl
This paper presents the novel La-belSOM method, which automatically selects the most descriptive features of the input patterns mapped onto a particular unit of the map, thus making the associations between the various clusters within the map explicit.