Abstract
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).
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© 1995 Springer-Verlag New York, Inc.
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Mucha, HJ. (1995). XClust: Clustering in an Interactive Way. In: XploRe: An Interactive Statistical Computing Environment. Statistics and Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4214-7_8
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DOI: https://doi.org/10.1007/978-1-4612-4214-7_8
Publisher Name: Springer, New York, NY
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