Description Usage Arguments Value Examples

`clustering`

is a convenience function that implements two frequently used ways of clustering conceptmaps directly.
The first is clustering using the MBMM algorithm and the concept matrix, the second is clustering using the PAM algorithm and
the graph similarity matrix.

1 | ```
clustering(maps, method = c("MBMM", "PAM"), min = 1, max = 10)
``` |

`maps` |
A conceptmaps object. |

`method` |
Either "PAM" or "MBMM", indicating which algorithm should be used. |

`min` |
The minimal number of components that is tested. For the PAM algorithm, 1 is not allowed. |

`max` |
The maximal number of components that is tested. |

The return value of either `MBM.cluster`

or `PAM.cluster`

, depending on the value of `method`

.

1 2 3 4 5 6 7 | ```
## Not run:
#Assuming that there are concept maps in folder "~/maps"
cms = read.folder.tgf("~/maps")
clustering(cms, method="MBMM")
## End(Not run)
``` |

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