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W.J. Borucki, NASA Ames Research Center
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, Dec. 1975, doi:10.1109/T-C.1975.224162
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