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: |
1.4-0 |
| Depends: |
R (≥ 2.14.0), graphics, grid, lattice, modeltools |
| Imports: |
methods, parallel, stats, stats4, class |
| Suggests: |
ellipse, clue, cluster, seriation, skmeans |
| Published: |
2018-09-24 |
| Author: |
Friedrich Leisch 
[aut, cre],
Evgenia Dimitriadou [ctb],
Bettina Gruen
[aut] |
| Maintainer: |
Friedrich Leisch <Friedrich.Leisch at R-project.org> |
| License: |
GPL-2 |
| NeedsCompilation: |
yes |
| Citation: |
flexclust citation info |
| Materials: |
NEWS |
| In views: |
Cluster |
| CRAN checks: |
flexclust results |