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Issue No.12 - December (1975 vol.24)
pp: 1183-1191
W.J. Borucki , NASA Ames Research Center
ABSTRACT
A technique is presented that uses both cluster analysis and a Monte Carlo significance test of clusters to discover associations between variables in multidimensional data. The method is applied to an example of a noisy function in three-dimensional space, to a sample from a mixture of three bivariate normal distributions, and to the well-known Fisher's Iris data.
INDEX TERMS
Clustering algorithms, data analysis, multivariate analysis, nonlinear data structures, pattern recognition.
CITATION
W.J. Borucki, D.H. Card, G.C. Lyle, "A Method of Using Cluster Analysis to Study Statistical Dependence in Multivariate Data", IEEE Transactions on Computers, vol.24, no. 12, pp. 1183-1191, December 1975, doi:10.1109/T-C.1975.224162
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