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