bclust | R Documentation |

Cluster the data in `x`

using the bagged clustering
algorithm. A partitioning cluster algorithm such as
`kmeans`

is run repeatedly on bootstrap samples from the
original data. The resulting cluster centers are then combined using
the hierarchical cluster algorithm `hclust`

.

```
bclust(x, centers=2, iter.base=10, minsize=0,
dist.method="euclidean",
hclust.method="average", base.method="kmeans",
base.centers=20, verbose=TRUE,
final.kmeans=FALSE, docmdscale=FALSE,
resample=TRUE, weights=NULL, maxcluster=base.centers, ...)
hclust.bclust(object, x, centers, dist.method=object$dist.method,
hclust.method=object$hclust.method, final.kmeans=FALSE,
docmdscale = FALSE, maxcluster=object$maxcluster)
## S3 method for class 'bclust'
plot(x, maxcluster=x$maxcluster, main, ...)
centers.bclust(object, k)
clusters.bclust(object, k, x=NULL)
```

`x` |
Matrix of inputs (or object of class |

`centers, k` |
Number of clusters. |

`iter.base` |
Number of runs of the base cluster algorithm. |

`minsize` |
Minimum number of points in a base cluster. |

`dist.method` |
Distance method used for the hierarchical
clustering, see |

`hclust.method` |
Linkage method used for the hierarchical
clustering, see |

`base.method` |
Partitioning cluster method used as base algorithm. |

`base.centers` |
Number of centers used in each repetition of the base method. |

`verbose` |
Output status messages. |

`final.kmeans` |
If |

`docmdscale` |
Logical, if |

`resample` |
Logical, if |

`weights` |
Vector of length |

`maxcluster` |
Maximum number of clusters memberships are to be computed for. |

`object` |
Object of class |

`main` |
Main title of the plot. |

`...` |
Optional arguments top be passed to the base method
in |

First, `iter.base`

bootstrap samples of the original data in
`x`

are created by drawing with replacement. The base cluster
method is run on each of these samples with `base.centers`

centers. The `base.method`

must be the name of a partitioning
cluster function returning a list with the same components as the
return value of `kmeans`

.

This results in a collection of ```
iter.base *
base.centers
```

centers, which are subsequently clustered using
the hierarchical method `hclust`

. Base centers with less
than `minsize`

points in there respective partitions are removed
before the hierarchical clustering.

The resulting dendrogram is then cut to produce `centers`

clusters. Hence, the name of the argument `centers`

is a little
bit misleading as the resulting clusters need not be convex, e.g.,
when single linkage is used. The name was chosen for compatibility
with standard partitioning cluster methods such as
`kmeans`

.

A new hierarchical clustering (e.g., using another
`hclust.method`

) re-using previous base runs can be
performed by running `hclust.bclust`

on the return value of
`bclust`

.

`bclust`

and `hclust.bclust`

return objects of class
`"bclust"`

including the components

`hclust` |
Return value of the hierarchical clustering of the
collection of base centers (Object of class |

`cluster` |
Vector with indices of the clusters the inputs are assigned to. |

`centers` |
Matrix of centers of the final clusters. Only useful, if the hierarchical clustering method produces convex clusters. |

`allcenters` |
Matrix of all |

Friedrich Leisch

Friedrich Leisch. Bagged clustering. Working Paper 51, SFB “Adaptive Information Systems and Modeling in Economics and Management Science”, August 1999. https://epub.wu.ac.at/1272/1/document.pdf

`hclust`

, `kmeans`

,
`boxplot.bclust`

```
data(iris)
bc1 <- bclust(iris[,1:4], 3, base.centers=5)
plot(bc1)
table(clusters.bclust(bc1, 3))
centers.bclust(bc1, 3)
```

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