flexclust: 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.
| Version: |
1.2-2 |
| Depends: |
graphics, grid, lattice, modeltools (≥ 0.2-16) |
| Imports: |
methods, stats, stats4 |
| Suggests: |
ellipse, clue, cluster, seriation, multicore |
| Published: |
2009-11-06 |
| Author: |
Friedrich Leisch, with contributions by Evgenia Dimitriadou. |
| Maintainer: |
Friedrich Leisch <Friedrich.Leisch at R-project.org> |
| License: |
GPL-2 |
| Citation: |
flexclust citation info |
| In views: |
Cluster |
| CRAN checks: |
flexclust results |
Downloads:
Reverse dependencies: