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DSC Interface for EMM and tNN (package stream)

Source: R/DSC_EMM.R, R/DSC_tNN.R
DSC_EMM.Rd

Provides Data Stream Clusterer (DSC) interfaces for EMM and tNN so they can be used in the stream framework.

Usage

DSC_EMM(
  formula = NULL,
  threshold = 0.2,
  measure = "euclidean",
  distFun = NULL,
  centroids = identical(tolower(measure), "euclidean"),
  lambda = 0
)

get_EMM(dsc)

set_EMM(dsc, x)

DSC_tNN(
  formula = NULL,
  threshold = 0.2,
  measure = "euclidean",
  centroids = identical(tolower(measure), "euclidean"),
  lambda = 0
)

Arguments

formula

NULL to use all features in the stream or a model formula of the form ~ X1 + X2 to specify the features used for clustering. Only ., + and - are currently supported in the formula.

threshold

A "numeric" with the dissimilarity threshold used by the clustering algorithm for assigning a new observation to existing clusters.

measure

A "character" containing the name of the dissimilarity measure used (see dist in proxy for available measures).

distFun

Specify a function passed on as method to dist in proxy (see dist in proxy). The character string passed on as measure will be used as the measure's name.

centroids

A "logical" indicating if centroids are used for clusters. If FALSE, pseudo medians (first observation of a cluster) are used to represent a cluster.

lambda

A "numeric" specifying the rate for fading.

dsc

an object of class "DSC_EMM".

x

an object of class "EMM"

Value

An object of class "DSC_EMM" or "DSC_tNN".

Details

DSC_tNN and DSC_EMM wrap the clustering algorithms so they can be used with the stream framework. See DSC for details.

get_EMM() and set_EMM() can be used to access the EMM object inside the DSC_EMM object.

Examples

library(stream)
#> 
#> Attaching package: ‘stream’
#> The following object is masked from ‘package:rEMM’:
#> 
#>     nclusters

### tNN clustering example

stream <- DSD_Gaussians()
stream
#> Gaussian Mixture (d = 2, k = 3) 
#> Class: DSD_Gaussians, DSD_R, DSD 

cl <- DSC_tNN(threshold = .1)
cl
#> Threshold Nearest Neighbor clustering 
#> Class: DSC_tNN, DSC_Micro, DSC_R, DSC 
#> Number of micro-clusters: 0 

update(cl, stream, 100)
cl
#> Threshold Nearest Neighbor clustering 
#> Class: DSC_tNN, DSC_Micro, DSC_R, DSC 
#> Number of micro-clusters: 8 

get_centers(cl)
#>           X1          X2
#> 1 0.17045402  0.04697728
#> 2 0.33255884  0.40136096
#> 3 0.71419979  0.88774672
#> 4 0.80863360  0.85132058
#> 5 0.23191398  0.41267140
#> 6 0.64530570  0.96565039
#> 7 0.24024356 -0.08468371
#> 8 0.09777111  0.11740419
get_weights(cl)
#>  1  2  3  4  5  6  7  8 
#> 26 32  8 22  6  3  2  1 

plot(cl, stream)


## EMM clustering example
data("EMMsim")
plot(EMMsim_train, pch = NA)
lines(EMMsim_train, col = "gray")
points(EMMsim_train, pch = EMMsim_sequence_train)


stream <- DSD_Memory(EMMsim_train)
stream
#> Memorized Stream 
#> Class: DSD_Memory, DSD_R, DSD 
#> Contains 200 data points - currently at position 1 - loop is FALSE 

cl <- DSC_EMM(threshold = 0.1, measure = "euclidean", lambda = .1)
update(cl, stream, n = 200)
cl
#> Extensible Markov Model 
#> Class: DSC_EMM, DSC_Micro, DSC_R, DSC 
#> Number of micro-clusters: 28 

reset_stream(stream)
plot(cl, stream, n = 200, method = "pca")


# inspect and recluster the EMM in the DSC_EMM object
emm <- get_EMM(cl)
plot(emm)


emm <- recluster_hclust(emm, k = 4, method = "average")
plot(emm)


set_EMM(cl, emm)

reset_stream(stream)
plot(cl, stream, n = 200, method = "pca")