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

A hierarchical clustering of variables using `hclust`

is performed using
1 - the absolute correlation as a distance measure between tow variables.

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`x` |
Either a data frame or a matrix consisting of numerical attributes. |

`cl` |
Optional vector of ty factor indicating class levels, if class specific correlations should to be considered. |

`method` |
Linkage to be used for clustering. Default is |

`selection` |
If |

`mincor` |
Adds a horizontal line for this correlation. |

`...` |
passed to underlying plot functions. |

Each cluster consists of a set of correlated variables according to the chosen clustering criterion.
The default criterion is ‘`complete`

’. This choice is meaningful as it represents the
*minimum absolute correlation* between all variables of a cluster.

The data set is split into numerics and factors two separate clustering models are built, depending on the variable type.
For factors distances are computed based on 1-Cramer's V statistic using `chisq.test`

.
For a large number of factor variables this might take some time.
The resulting trees can be plotted using `plot`

.

Further proceeding would consist in chosing one variable of each cluster to obtain a
subset of rather uncorrelated variables for further analysis.
An automatic variable selection can be done using `cvtree`

and `xtractvars`

.

If an additional class vector `cl`

is given to the function for any two variables their minimum correlation over all classes is used.

Object of class `corclust`

.

`cor` |
Correlation matrix of numeric variables. |

`crv` |
Matrix of Cramer's V for factor variables. |

`cluster.numerics` |
Resulting hierarchical |

`cluster.factors` |
Resulting hierarchical |

`id.numerics` |
Variable IDs of numeric variables in |

`id.factors` |
Variable IDs of factor variables |

Gero Szepannek

Roever, C. and Szepannek, G. (2005): Application of a genetic algorithm to variable selection in fuzzy clustering. In C. Weihs and W. Gaul (eds), Classification - The Ubiquitous Challenge, 674-681, Springer.

`plot.corclust`

and `hclust`

for details on the clustering algorithm, and
`cvtree`

, `xtractvars`

for postprocessing.

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