Issue No. 12 - December (1975 vol. 24)
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.
Clustering algorithms, data analysis, multivariate analysis, nonlinear data structures, pattern recognition.
G. Lyle, W. Borucki and D. Card, "A Method of Using Cluster Analysis to Study Statistical Dependence in Multivariate Data," in IEEE Transactions on Computers, vol. 24, no. , pp. 1183-1191, 1975.