randomUniformForest (version 1.1.2)

update.unsupervised: Update Unsupervised Learning object

Description

Update unsupervised learning object with new data in order to achieve incremental learning. New MDS points are predicted with new data and learning of MDS points of the former unsupervised object.

Usage

## S3 method for class 'unsupervised':
update(object, X, 
	oldData = NULL, 
	mapAndReduce = FALSE, 
	updateModel = FALSE, 
	\dots)

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

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

Examples

## not run
## Water Treatment Plant Data Set
## Data can be download at https://archive.ics.uci.edu/ml/datasets/Water+Treatment+Plant

# URL = "http://archive.ics.uci.edu/ml/machine-learning-databases/water-treatment/"
# dataset = "water-treatment.data"

# X = read.table(paste(URL, dataset, sep=""), sep = ",")

## 1- Preprocessing
## first, look at the first column and format date
#  Dates = rm.string(as.character(X[,1]), "D-")
#  DatesAsStringTable = do.call(rbind, strsplit(Dates, "/"))
#  DatesasNumericTable = t(apply(DatesAsStringTable, 1, as.numeric))

##  Then, transform data as a R matrix and add new dates
#  XX = as.true.matrix(X)[,-1]
#  XX = cbind(DatesasNumericTable, XX)
#  colnames(XX)[1:3] = c("day", "month", "year")

# Look the new data
# head(XX)
# str(XX)

## and fill missing values,
## X.imputed = fillNA2.randomUniformForest(XX)

## 2 - run unsupervised analysis on the first half of dataset 
##
# subset.1 = 1:floor(nrow(X.imputed)/2)
# WaterTreatment.model.1 = unsupervised.randomUniformForest(X.imputed, subset = subset.1, 
# baseModel = "proximityThenDistance", seed = 2014)

## assess roughly the model and visualize
#  WaterTreatment.model.1

## 3 - update model with the second half of dataset
# WaterTreatment.updated = update.unsupervised(WaterTreatment.model.1, 
# X.imputed[-subset.1,], oldData = X.imputed[-subset.1,])

# WaterTreatment.updated

## view how MDS points have been learned :
## first component
# WaterTreatment.updated$largeDataLearningModel[[1]]

# second component
# WaterTreatment.updated$largeDataLearningModel[[2]]