Description Usage Arguments Details Value References See Also Examples

This function facilitates the selection of the appropriate number of clusters and dimensions for joint dimension reduction and clustering methods.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
tuneclus(data, nclusrange = 3:4, ndimrange = 2:3,
method = c("RKM","FKM","mixedRKM","mixedFKM","clusCA","iFCB","MCAk"),
criterion = "asw", dst = "full", alpha = NULL, alphak = NULL,
center = TRUE, scale = TRUE, rotation = "none", nstart = 100,
smartStart = NULL, seed = NULL)
## S3 method for class 'tuneclus'
print(x, ...)
## S3 method for class 'tuneclus'
summary(object, ...)
## S3 method for class 'tuneclus'
fitted(object, mth = c("centers", "classes"), ...)
``` |

`data` |
Continuous, Categorical ot Mixed data set |

`nclusrange` |
An integer vector with the range of numbers of clusters which are to be compared by the cluster validity criteria. Note: the number of clusters should be greater than one |

`ndimrange` |
An integer vector with the range of dimensions which are to be compared by the cluster validity criteria |

`method` |
Specifies the method. Options are |

`criterion` |
One of |

`dst` |
Specifies the data used to compute the distances between objects. Options are |

`alpha` |
Adjusts for the relative importance of (mixed) RKM and FKM in the objective function; |

`alphak` |
Non-negative scalar to adjust for the relative importance of MCA ( |

`center` |
A logical value indicating whether the variables should be shifted to be zero centered (default = |

`scale` |
A logical value indicating whether the variables should be scaled to have unit variance before the analysis takes place (default = |

`rotation` |
Specifies the method used to rotate the factors. Options are none for no rotation, varimax for varimax rotation with Kaiser normalization and promax for promax rotation (default = |

`nstart` |
Number of starts (default = 100) |

`smartStart` |
If |

`seed` |
An integer that is used as argument by |

`x` |
For the |

`object` |
For the |

`mth` |
For the |

`...` |
Not used |

For the K-means part, the algorithm of Hartigan-Wong is used by default.

The hidden `print`

and `summary`

methods print out some key components of an object of class `tuneclus`

.

The hidden `fitted`

method returns cluster fitted values. If method is `"classes"`

, this is a vector of cluster membership (the cluster component of the "tuneclus" object). If method is `"centers"`

, this is a matrix where each row is the cluster center for the observation. The rownames of the matrix are the cluster membership values.

`clusobjbest` |
The output of the optimal run of |

`nclusbest` |
The optimal number of clusters |

`ndimbest` |
The optimal number of dimensions |

`critbest` |
The optimal criterion value for |

`critgrid` |
Matrix of size |

`criterion` |
"asw" for average Silhouette width or "ch" for "Calinski-Harabasz" |

`cluasw` |
Average Silhouette width values of each cluster, when criterion = "asw" |

Calinski, R.B., and Harabasz, J., (1974). A dendrite method for cluster analysis. *Communications in Statistics*, 3, 1-27.

Kaufman, L., and Rousseeuw, P.J., (1990). *Finding Groups in Data: An Introduction to Cluster Analysis*. Wiley, New York.

`global_bootclus`

, `local_bootclus`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ```
# Reduced K-means for a range of clusters and dimensions
data(macro)
# Cluster quality assessment based on the average silhouette width in the low dimensional space
# nstart = 1 for speed in example
# use more for real applications
bestRKM = tuneclus(macro, 3:4, 2:3, method = "RKM",
criterion = "asw", dst = "low", nstart = 1, seed = 1234)
bestRKM
#plot(bestRKM)
# Cluster Correspondence Analysis for a range of clusters and dimensions
data(bribery)
# Cluster quality assessment based on the Callinski-Harabasz index in the full dimensional space
bestclusCA = tuneclus(bribery, 4:5, 3:4, method = "clusCA",
criterion = "ch", nstart = 20, seed = 1234)
bestclusCA
#plot(bestclusCA, cludesc = TRUE)
# Mixed reduced K-means for a range of clusters and dimensions
data(diamond)
# Cluster quality assessment based on the average silhouette width in the low dimensional space
# nstart = 5 for speed in example
# use more for real applications
bestmixedRKM = tuneclus(diamond[,-7], 3:4, 2:3,
method = "mixedRKM", criterion = "asw", dst = "low",
nstart = 5, seed = 1234)
bestmixedRKM
#plot(bestmixedRKM)
``` |

clustrd documentation built on May 8, 2019, 5:03 p.m.

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