Supervised clustering using decision trees and decision graphs: An ecological comparison

In this paper, we outline some of the problems in computer learning, particularly with respect to decision trees. We then consider how, in some cases, a decision graph may provide a solution to some of these problems. We compare a decision graph analysis with a decision tree analysis of salt marsh data, predicting predetermined vegetation types from environmental properties. All analyses use a minimum message length criterion to select an optimal model within a class, thereby avoiding subjective decisions. Minimum message length also provides a criterion for choosing between the model classes of tree and graph. ition to the computational evaluation of models, we examine the ecological implications of the selected solutions. Even if sub-optimal, it is possible that a result can contribute to understanding of the underlying real system.