optCluster-class | R Documentation |
The class "optCluster"
contains the dataset,
clustering results, validation measures, ranked lists of clustering algorithms,
ordered lists of validation scores, and final rank aggregation results from
the function optCluster
.
The function optCluster
creates objects of the class
"optCluster"
.
inputData
:Object of class "matrix"
containing the original dataset.
clVal
:Object of class "clValid"
containing the clustering results and validation measures from the
internal clValid
function.
ranksWeights
:Object of class "list"
containing the ordered ranks of clustering algorithms and
the ordered validation scores for each measure.
rankAgg
:Object of class "raggr"
containing the rank aggregation results from the internal
RankAggreg
function.
signature(object = "optCluster")
: Returns
the cluster assignment corresponding to the optimal clustering algorithm
and number of clusters.
signature(object = "optCluster")
: Returns the
original dataset as an object of class "matrix"
.
signature(object = "optCluster")
: Returns an
object of class "clValid"
.
signature(object = "optCluster")
: Returns the
ranked lists of clustering algorithms for each validation measure.
signature(object = "optCluster")
: Returns the
ordered lists of scores for each validation measure.
signature(object = "optCluster")
: Returns an
object of class "raggr"
.
signature(object = "optCluster")
: Returns the
name of the optimal clustering algorithm and number of clusters.
signature(object = "optCluster")
: Returns the
names of the validation measures used.
signature(object = "optCluster")
: Returns the
names of the clustering algorithms used.
signature(object = "optCluster")
: Returns an
object of the class corresponding to the selected clustering method for each
number of cluster in the analysis. If provided k
,
the object and clustering assignment for the corresponding method and
number of clusters is returned.
Additional arguments:
method = methodNames(object)
The clustering algorithm to extract. The selection of only one algorithm is allowed.
k = NULL
The number of clusters to extract. The selection of only one number of clusters is allowed.
signature(object = "optCluster")
: Returns the
scores from the selected validation measure(s).
Additional arguments:
measures = measureNames(object)
The validation measure(s) to extract.
signature(object = "optCluster")
: Returns the
optimal score for each validation measure as well as the
corresponding clustering algorithm and number of clusters.
signature(x = "optCluster")
: Print method for class
"optCluster"
.
signature(object = "optCluster")
: Same as print.
signature(object = "optCluster")
: Summary method
for class "optCluster"
.
Sekula, M., Datta, S., and Datta, S. (2017). optCluster: An R package for determining the optimal clustering algorithm. Bioinformation, 13(3), 101. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5450252
Brock, G., Pihur, V., Datta, S. and Datta, S. (2008). clValid: An R Package for Cluster Validation. Journal of Statistical Software 25(4), https://www.jstatsoft.org/v25/i04.
Datta, S. and Datta, S. (2003). Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 19(4): 459-466.
Pihur, V., Datta, S. and Datta, S. (2007). Weighted rank aggregation of cluster validation measures: A Mounte Carlo cross-entropy approach. Bioinformatics 23(13): 1607-1615.
Pihur, V., Datta, S. and Datta, S. (2009). RankAggreg, an R package for weighted rank aggregation. BMC Bioinformatics, 10:62, https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-10-62.
For details on the function optCluster
see optCluster
.
For a description of the clValid
function, including all available arguments that can be
passed to it, see clValid
in the clValid package.
For a desciption of the class "clValid"
including all available methods see
clValid-class
.
For a description of the RankAggreg
function, including all available arguments that can be
passed to it, see RankAggreg
in the RankAggreg package.
## This example may take a few minutes to compute ## Obtain Dataset data(arabid) ## Normalize Data with Respect to Library Size obj <- t(t(arabid)/colSums(arabid)) ## Analysis of Normalized Data using Internal and Stability Measures norm1 <- optCluster(obj, 2:4, clMethods = "all") ## View results norm1 topMethod(norm1) summary(norm1) optimalScores(norm1) optAssign(norm1) ## Extract cluster results for kmeans and all numbers of clusters clusterResults(norm1, method = "kmeans") ## Extract cluster results for kmeans and 3 clusters only clusterResults(norm1, method = "kmeans", k = 3) ## Extract all validation scores valScores(norm1) ## Extract validations scores for APN and ADM only valScores(norm1, measures = c("APN", "ADM")) ## Extract additional information from slots methodNames(norm1) measureNames(norm1) methodRanks(norm1) scoreRanks(norm1)
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