Description Usage Arguments Details Value Author(s) References See Also Examples

Cluster the data in `x`

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

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

.

1 2 3 4 5 6 7 8 9 10 | ```
bclust(x, k = 2, base.iter = 10, base.k = 20, minsize = 0,
dist.method = "euclidian", hclust.method = "average",
FUN = "cclust", verbose = TRUE, final.cclust = FALSE,
resample = TRUE, weights = NULL, maxcluster = base.k, ...)
## S4 method for signature 'bclust,missing'
plot(x, y, maxcluster = x@maxcluster, main = "", ...)
## S4 method for signature 'bclust,missing'
clusters(object, newdata, k, ...)
## S4 method for signature 'bclust'
parameters(object, k)
``` |

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

`k` |
Number of clusters. |

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

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

`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 |

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

`verbose` |
Output status messages. |

`final.cclust` |
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 |

`y` |
Missing. |

`newdata` |
An optional data matrix with the same number of columns as the cluster centers. If omitted, the fitted values are used. |

First, `base.iter`

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.k`

centers. The `base.method`

must be the name of a partitioning
cluster function returning an object with the same slots as the
return value of `cclust`

.

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 `k`

clusters.

`bclust`

returns objects of class
`"bclust"`

including the slots

`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. http://epub.wu.ac.at/1272/1/document.pdf

Sara Dolnicar and Friedrich Leisch. Winter tourist segments in Austria: Identifying stable vacation styles using bagged clustering techniques. Journal of Travel Research, 41(3):281-292, 2003.

1 2 3 4 5 6 |

```
Loading required package: grid
Loading required package: lattice
Loading required package: modeltools
Loading required package: stats4
Committee Member:
1 2 3 4 5 6 7 8 9 10
Computing Hierarchical Clustering
1 2 3
69 50 31
Sepal.Length Sepal.Width Petal.Length Petal.Width
[1,] 5.868125 2.737226 4.383671 1.4390382
[2,] 5.034654 3.451956 1.462782 0.2506548
[3,] 7.077667 3.136041 5.946642 2.1197749
```

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