Model{based clustering with the assignment problem
G. Ritter · 2006
We establish a statistical model for clustering in the presence of gross outliers and derive its ML and MAP clustering estimators. Their computation leads to constrained trimming algorithms of k{means type. One of them includes a {assignment problem known from combinatorial optimization. We also estimate the numbers of clusters and outliers with the algorithms obtained and illustrate them with some examples.