The study of clustering and classification of uncertain data addresses the challenges posed by imprecise, noisy, or inherently probabilistic measurements common in many modern data acquisition ...

The supervised counterpart for clustering is called classification. With classification you know the groups that a large set of entities belong to, and you want to train an algorithm to classify ...

Classification of deforestation is one of the primary objectives in the analysis of remotely sensed data. The present study focuses on monitoring accurate results of deforestation and forest ...

(1) The application of hierarchical classification to ecological community data is examined, using a variety of classification techniques and test data sets. Problems discussed include: (a) the choice ...

Minimum discrimination information provides a useful generalization of likelihood methodology for classification and clustering of multivariate time series. Discrimination between different classes of ...