This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
November 1969 (vol. 18 no. 11)
pp. 987-991
The unsupervised estimation problem has been conveniently formulated in terms of a mixture density. It has been shown that a criterion naturally arises whose maximum defines the Bayes minimum risk solution. This criterion is the expected value of the natural log of the mixture density. By making the assumptions that the component densities in the mixture are truncated Gaussian, the criterion has a greatly simplified form. This criterion can be used to resolve mixtures when the number of classes as well as the class covariances are unknown. In this paper a technique is presented where an assumed test covariance is supplied by an experimenter who uses a test function as a "portable magnifying glass" to examine data. Because the experimenter supplies the covariance and thus the test function, the technique is especially suited for interactive data analysis.
Index Terms:
Clustering, computer display of mixed data, computer graphics in pattern recognition, interactive data analysis, interactive pattern recognition system, mixture density, pattern recognition, sorting data unsupervised estimation of densities.
Citation:
E.A. Patrick, F.P. Fischer, "Cluster Mapping with Experimental Computer Graphics," IEEE Transactions on Computers, vol. 18, no. 11, pp. 987-991, Nov. 1969, doi:10.1109/T-C.1969.222567
Usage of this product signifies your acceptance of the Terms of Use.