Package: vegclust 2.0.3

Introduction | What is this tutorial about? | Example vegetation data | Clustering methods in vegclust | Resemblance space | Prototype-based clustering | Clustering models | Hard (crisp) or fuzzy memberships | Centroids or medoids | Partitive clustering | Noise clustering | Possibilistic clustering | Dissimilarity-based duals | Medoid-based clustering and dissimilarity matrices | Centroid-based clustering and dissimilarity matrices | Managing vegetation classifications | Creating classifications: vegclust and vegclustdist() | The K-means model | The Fuzzy C-means model | The Noise clustering model | Medoid-based clustering | Supervised classification: as.vegclust() and vegclass() | Extending vegetation classifications | Conforming vegetation data sets | Re-calculating the centroids of the initial classification | Calling vegclust with fixed prototypes | Extending or refining classifications? | Using vegclustdist() with fixed prototypes | Cluster characterization | Cluster prototypes: clustcentroid() and clustmedoid() | Cluster internal variability: clustvar() | Distance between clusters: interclustdist() | Constancy classes: clustconst() | Bibliography

Introduction | Post-fire vegetation regeneration data | Cumulative abundance profiles | Cumulative abundance surfaces | Dissimilarities in structure and composition | Classification of vegetation stands | References