| Type: | Package |
| Title: | Selection of Number of Clusters via Normalized Clustering Instability |
| Version: | 0.2-2 |
| Date: | 2018-06-19 |
| Author: | Jonas M. B. Haslbeck, Dirk U. Wulff |
| Maintainer: | Jonas M. B. Haslbeck <jonas.haslbeck@gmail.com> |
| Description: | Selection of the number of clusters in cluster analysis using stability methods. |
| BugReports: | https://github.com/jmbh/cstab/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Depends: | R (≥ 3.1.0), Rcpp (≥ 0.11.4) |
| Imports: | cluster, fastcluster, |
| LinkingTo: | Rcpp |
| RoxygenNote: | 6.0.1 |
| NeedsCompilation: | yes |
| Packaged: | 2018-06-19 16:35:53 UTC; jmb |
| Repository: | CRAN |
| Date/Publication: | 2018-06-19 16:59:43 UTC |
cstab: Selection of number of clusters via normalized clustering instability
Description
Selection of the number of clusters in cluster analysis using stability methods.
Details
| Package: | cstab |
| Type: | Package |
| Version: | 0.01 |
| Date: | 2016-07-26 |
| License: | GPL (>= 2) |
Author(s)
Dirk U. Wulff <dirk.wulff@gmail.com> Jonas M. B. Haslbeck <jonas.haslbeck@gmail.com>
Selection of number of clusters via distance-based measures
Description
Selection of number of clusters via gap statistic, jump statistic, and slope statistic
Usage
cDistance(data, kseq, method = "kmeans", linkage = "complete",
kmIter = 10, gapIter = 10)
Arguments
data |
a n x p data matrix of type numeric. |
kseq |
a vector with considered numbers clusters k > 1 |
method |
character string indicating the clustering algorithm. 'kmeans' for the k-means algorithm, 'hierarchical' for hierarchical clustering. |
linkage |
character specifying the linkage criterion, in case
|
kmIter |
integer specifying the the number of restarts of the k-means algorithm in order to avoid local minima. |
gapIter |
integer specifying the number of simulated datasets to compute the gap statistic (see Tibshirani et al., 2001). |
Value
a list with the optimal numbers of cluster determined by the gap statistic
(Tibshirani et al., 2001), the jump Statistic (Sugar & James, 2011) and the
slope statistic (Fujita et al., 2014). Along the function returns the gap,
jump and slope for each k in kseq.
Author(s)
Dirk U. Wulff <dirk.wulff@gmail.com> Jonas M. B. Haslbeck <jonas.haslbeck@gmail.com>
References
Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423.
Sugar, C. A., & James, G. M. (2011). Finding the number of clusters in a dataset. Journal of the American Statistical Association, 98(463), 750-763,
Fujita, A., Takahashi, D. Y., & Patriota, A. G. (2014). A non-parametric method to estimate the number of clusters. Computational Statistics & Data Analysis, 73, 27-39.
Examples
## Not run:
# Generate Data from Gaussian Mixture
s <- .1
n <- 50
data <- rbind(cbind(rnorm(n, 0, s), rnorm(n, 0, s)),
cbind(rnorm(n, 1, s), rnorm(n, 1, s)),
cbind(rnorm(n, 0, s), rnorm(n, 1, s)),
cbind(rnorm(n, 1, s), rnorm(n, 0, s)))
plot(data)
# Selection of Number of Clusters using Distance-based Measures
cDistance(data, kseq=2:10)
## End(Not run)
Selection of number of clusters via clustering instability
Description
Selection of number of clusters via model-based or model-free, normalized or unnormalized clustering instability.
Usage
cStability(data, kseq = 2:20, nB = 10, norm = TRUE, predict = TRUE,
method = "kmeans", linkage = "complete", kmIter = 5, pbar = TRUE)
Arguments
data |
a n x p data matrix of type numeric. |
kseq |
a vector with considered numbers clusters k > 1 |
nB |
an integer specifying the number of bootstrap comparisons. |
norm |
logical specifying whether the instability path should be normalized. If TRUE, the instability path is normalized, accounting for a trivial decrease in instability due to a increasing k (see Haslbeck & Wulff, 2016). |
predict |
boolean specifying whether the model-based or the model-free variant should be used (see Haslbeck & Wulff, 2016). |
method |
character string specifying the clustering algorithm. 'kmeans' for the k-means algorithm, 'hierarchical' for hierarchical clustering. |
linkage |
character specifying the linkage criterion, in case
|
kmIter |
integer specifying the the number of restarts of the k-means algorithm in order to avoid local minima. |
pbar |
logical |
Value
a list that contains the optimal k selected by the unnormalized and normalized instability method. It also includes a vector containing the averaged instability path (over bootstrap samples) and a matrix containing the instability path of each bootstrap sample for both the normalized and the unnormalized method.
Author(s)
Dirk U. Wulff <dirk.wulff@gmail.com> Jonas M. B. Haslbeck <jonas.haslbeck@gmail.com>
References
Ben-Hur, A., Elisseeff, A., & Guyon, I. (2001). A stability based method for discovering structure in clustered data. Pacific symposium on biocomputing, 7, 6-17.
Tibshirani, R., & Walther, G. (2005). Cluster validation by prediction strength. Journal of Computational and Graphical Statistics, 14(3), 511-528.
Examples
## Not run:
# Generate Data from Gaussian Mixture
s <- .1
n <- 50
data <- rbind(cbind(rnorm(n, 0, s), rnorm(n, 0, s)),
cbind(rnorm(n, 1, s), rnorm(n, 1, s)),
cbind(rnorm(n, 0, s), rnorm(n, 1, s)),
cbind(rnorm(n, 1, s), rnorm(n, 0, s)))
plot(data)
# Selection of Number of Clusters using Instability-based Measures
stab_obj <- cStability(data, kseq=2:10)
print(stab_obj)
## End(Not run)
Cluster example
Description
An example, 2-dimensional dataset containing the 100 points for each of five bivariate normal distributions arranged equidistant along the outline of a circle.
Usage
cluster_example
Format
An object of class matrix with 500 rows and 2 columns.
Details
To inspect execute plot(cluster_example).
Create lookup table
Description
Create lookup table for faculties
Usage
lookup(n = 10000L, root = 200)
Arguments
n |
integer specifying the number of |
root |
numeric specifying the root used to avoid machine limit. |
Plot method for cstab objects
Description
plot.cstab plots instability path.
Usage
## S3 method for class 'cstab'
plot(x, ...)
Arguments
x |
a cstab object (output of functions |
... |
additional arguments passed to print. |
Author(s)
Jonas M. B. Haslbeck <jonas.haslbeck@gmail.com> Dirk U. Wulff <dirk.wulff@gmail.com>
Print method for cstab objects
Description
print.cstab prints key variables of cstab objects.
Usage
## S3 method for class 'cstab'
print(x, ...)
Arguments
x |
a cstab object (output of functions |
... |
additional arguments passed to print. |
Author(s)
Jonas M. B. Haslbeck <jonas.haslbeck@gmail.com> Dirk U. Wulff <dirk.wulff@gmail.com>