randomUniformForest (version 1.1.2)

combine.unsupervised: Combine Unsupervised Learning objects

Description

Combine unsupervised learning objects in order to achieve incremental learning. Only the MDS points are used then standardized before calling a clustering algorithm on all. Note that the function is currently highly experimental with, currently, a lack of applications.

Usage

combine.unsupervised(...)

Value

• 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.

See Also

update.unsupervised, modifyClusters, mergeClusters, clusteringObservations, as.supervised

Examples

## 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, 
# baseModel = "proximityThenDistance")

## 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)

## 3 - combine
# wineQualityRed.combinedModel = 
# combine.unsupervised(wineQualityRed.model.1, wineQualityRed.model.2)

## visualize and plot
# wineQualityRed.combinedModel
# plot(wineQualityRed.combinedModel)

# compare with the full data
# wineQualityRed.model = unsupervised.randomUniformForest(X, 
# baseModel = "proximityThenDistance")