1995 | OriginalPaper | Buchkapitel
XClust: Clustering in an Interactive Way
verfasst von : Hans-Joachim Mucha
Erschienen in: XploRe: An Interactive Statistical Computing Environment
Verlag: Springer New York
Enthalten in: Professional Book Archive
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Cluster analysis attempts to detect structures in the data. Some of the most important and widely applicable clustering techniques are partitioning methods and hierarchical clustering algorithms. Well-known methods from both these families are available simply by commands in the interactive statistical computing environment XploRe. Moreover, new adaptive clustering methods (which are often much more stable against random selection or small random disturbance of the data, and which seem to be a little bit intelligent because of their ability for learning the appropriate distance measures) can be carried out by macros. Additionally, the importance of each variable involved in clustering can be evaluated by taking into account its adaptive weight. The adaptive techniques are based on adaptive distances which should also be used in order to obtain multivariate plots (Mucha, 1992).