View source: R/RandomUniformForestsCPP.R
combineUnsupervised | R Documentation |
Combine unsupervised learning objects in order to achieve incremental learning. Only the MDS (spectral) points are used before calling a clustering algorithm on all. Note that the function is currently highly experimental with a lack of applications.
combineUnsupervised(...)
... |
(enumeration of) objects of class unsupervised, coming from |
An object of class unsupervised, which is a list with the following components:
proximityMatrix |
the resulted dissimilarity matrix. |
MDSModel |
the resulted Multidimensional scaling model. |
unsupervisedModel |
the resulted unsupervised model with clustered observations in unsupervisedModel$cluster. |
largeDataLearningModel |
if the dataset is large, the resulted model that learned a sample of the MDS points, and predicted others points. |
gapStatistics |
if K-means algorithm has been called, the results of the gap statistic. Otherwise NULL. |
rUFObject |
Random Uniform Forests object. |
nbClusters |
Number of clusters found. |
params |
options of the model. |
Saip Ciss saip.ciss@wanadoo.fr
update.unsupervised
, modifyClusters
, mergeClusters
,
splitClusters
, clusteringObservations
, as.supervised
## not run ## Wine Quality Data Set ## http://archive.ics.uci.edu/ml/datasets/Wine+Quality # data(wineQualityRed) # X = wineQualityRed[, -ncol(wineQualityRed)] ## 1 - run unsupervised analysis on the first half of dataset # subset.1 = 1:floor(nrow(X)/2) # wineQualityRed.model.1 = unsupervised.randomUniformForest(X, subset = subset.1, depth = 5) ## assess roughly the model and visualize # wineQualityRed.model.1 # plot(wineQualityRed.model.1) ## 2 - run unsupervised analysis on the second half of dataset # wineQualityRed.model.2 = unsupervised.randomUniformForest(X, subset = -subset.1, depth = 5) ## 2.1 if less clusters (than in 1) are got, split the one with the highest number of cases ## it is the second cluster in our case # wineQualityRed.model.2 = splitClusters(wineQualityRed.model.2, 2) ## roughly assess and, eventually, merge and split again (with different seeds) in order ## to be confident about the new clustering # wineQualityRed.model.2 ## 3 - combine # wineQualityRed.combinedModel = # combineUnsupervised(wineQualityRed.model.1, wineQualityRed.model.2) ## visualize and plot # wineQualityRed.combinedModel # plot(wineQualityRed.combinedModel) ## compare with the full data and same modelling # wineQualityRed.model = unsupervised.randomUniformForest(X, depth = 5) ## or increase depth (more computation and default option) for a more detailed model # wineQualityRed.model = unsupervised.randomUniformForest(X)
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