RT Journal Article
JF IEEE Transactions on Computers
YR 1969
VO 18
IS
SP 987
TI Cluster Mapping with Experimental Computer Graphics
A1 F.P. Fischer,
A1 E.A. Patrick,
K1 Clustering
K1 computer display of mixed data
K1 computer graphics in pattern recognition
K1 interactive data analysis
K1 interactive pattern recognition system
K1 mixture density
K1 pattern recognition
K1 sorting data unsupervised estimation of densities.
AB 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.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 0018-9340
LA English
DO 10.1109/T-C.1969.222567
LK http://doi.ieeecomputersociety.org/10.1109/T-C.1969.222567

RT Journal Article
JF IEEE Transactions on Computers
YR
VO 18
IS 11
SP 987
TI Cluster Mapping with Experimental Computer Graphics
AB 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.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 0018-9340
LA English
DO 10.1109/T-C.1969.222567
LK http://doi.ieeecomputersociety.org/10.1109/T-C.1969.222567