Population Mixture Models and Clustering Algorithms
Abstract
The problem of clustering individuals is considered within the context of a mixture of distributions. A modification of the usual approach to population mixtures is employed. As usual, a parametric family of distributions is considered, a set of parameter values being associated with each population. In addition, with each observation is associated an identification parameter, indicating from which population the observation arose. The resulting likelihood function is interpreted in terms of the conditional probability density of a sample from a mixture of populations, given the identification parameter of each observation. Clustering algorithms are obtained by applying a method of iterated maximum likelihood to this likelihood function.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 1973
- Accession Number
- AD0758654
Entities
People
- Stanley L. Sclove
Organizations
- Stanford University