Description Usage Arguments Value Examples

`PAM.cluster`

calculates a clustering using the PAM algorithm (k-medoids). The quality of the clustering is judged using the
G1 index.

1 | ```
PAM.cluster(data, min = 2, max = 10, metric = "manhattan")
``` |

`data` |
A numeric matrix. |

`min` |
The minimal number of components that is tested. Must be at least 2. |

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

`metric` |
If empty, data will be treated as a distance matrix. Otherwise, the value will be passed to the call of |

A list with 3 elements. The first element contains the optimal number of components according to the G1 index.
The second element contains a vector of the G1 values. The thrid element contains the clustering itself,
i.e. the return value of `PAM`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
## Not run:
#Random data generation, 10 dimensions, 500 observations, 2 clusters
require("gtools")
data = c()
p = 0.0
for (i in 1:2)
{
temp = c()
for (j in 1:10)
temp = cbind(temp, rbinom(250, 1, p+(i-1)*0.5+(0.025*i)*j))
data=rbind(data, temp)
}
data = data[permute(1:500),]
PAM.cluster(data)
## End(Not run)
``` |

comato documentation built on May 2, 2019, 6:52 a.m.

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