Mixture separation for mixed-mode data |
| |
Authors: | C. J. Lawrence W. J. Krzanowski |
| |
Affiliation: | (1) Department of Mathematical Statistics and Operational Research, University of Exeter, North Park Road, EX4 4QE Exeter, UK |
| |
Abstract: | One possible approach to cluster analysis is the mixture maximum likelihood method, in which the data to be clustered are assumed to come from a finite mixture of populations. The method has been well developed, and much used, for the case of multivariate normal populations. Practical applications, however, often involve mixtures of categorical and continuous variables. Everitt (1988) and Everitt and Merette (1990) recently extended the normal model to deal with such data by incorporating the use of thresholds for the categorical variables. The computations involved in this model are so extensive, however, that it is only feasible for data containing very few categorical variables. In the present paper we consider an alternative model, known as the homogeneous Conditional Gaussian model in graphical modelling and as the location model in discriminant analysis. We extend this model to the finite mixture situation, obtain maximum likelihood estimates for the population parameters, and show that computation is feasible for an arbitrary number of variables. Some data sets are clustered by this method, and a small simulation study demonstrates characteristics of its performance. |
| |
Keywords: | Cluster analysis Conditional Gaussian distribution EM algorithm graphical modelling location model mixture maximum likelihood simulation |
本文献已被 SpringerLink 等数据库收录! |
|