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.

- optAssign
`signature(object = "optCluster")`

: Returns the cluster assignment corresponding to the optimal clustering algorithm and number of clusters.- getDataset
`signature(object = "optCluster")`

: Returns the original dataset as an object of class`"matrix"`

.- getClValid
`signature(object = "optCluster")`

: Returns an object of class`"clValid"`

.- methodRanks
`signature(object = "optCluster")`

: Returns the ranked lists of clustering algorithms for each validation measure.- scoreRanks
`signature(object = "optCluster")`

: Returns the ordered lists of scores for each validation measure.- getRankAggreg
`signature(object = "optCluster")`

: Returns an object of class`"raggr"`

.- topMethod
`signature(object = "optCluster")`

: Returns the name of the optimal clustering algorithm and number of clusters.- measureNames
`signature(object = "optCluster")`

: Returns the names of the validation measures used.- methodNames
`signature(object = "optCluster")`

: Returns the names of the clustering algorithms used.- clusterResults
`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.

- valScores
`signature(object = "optCluster")`

: Returns the scores from the selected validation measure(s).

Additional arguments:

`measures = measureNames(object)`

The validation measure(s) to extract.

- optimalScores
`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"`

.- show
`signature(object = "optCluster")`

: Same as print.- summary
`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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