Flexible Cluster Algorithms

The main function kcca implements a general framework for k-centroids cluster analysis supporting arbitrary distance measures and centroid computation. Further cluster methods include hard competitive learning, neural gas, and QT clustering. There are numerous visualization methods for cluster results (neighborhood graphs, convex cluster hulls, barcharts of centroids, ...), and bootstrap methods for the analysis of cluster stability.

Tests Vignettes

Available Snapshots

This version of flexclust can be found in the following snapshots:


Imports/Depends/LinkingTo/Enhances (2)
  • R
  • modeltools
  • Suggests (4)
  • ellipse
  • clue
  • seriation
  • skmeans
  • Version History