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 ...

Clustering and classification represent the opportunity to apply categorical reasoning to vast data contexts we would otherwise find overwhelming.

Data mining techniques have been widely used for extracting knowledge from large amounts of data. Monitoring deforestation is utmost important for the developing countries.

(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 ...