Class

This class represents the extensible Markov Model. It consists of a simple data stream clustering algorithm (class "tNN") and a temporal layer (class "TRACDS").

Create a new object of class "EMM".

Usage

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

Arguments

threshold: Object of class "numeric" with the dissimilarity threshold used by the clustering algorithm for assigning a new observation to existing clusters.
measure: Object of class "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: Object of class "logical" indicating if centroids are used for clusters. If FALSE, pseudo medians (first observation of a cluster) are used to represent a cluster.
lambda: Object of class "numeric" specifying the rate for fading.
data: Initial data to build the EMM. This just calls build on the new EMM.

Value

An object of class "EMM".

Objects from the Class

Objects can be created using the creator function EMM or by directly calling new("EMM", ...). Most slots for the extended classes can be used as parameters for EMM.

Slots

The slots are described in corresponding the extended classes (see section Extends).

Extends

Class "tNN", directly. Class "TRACDS", directly.

Methods

copy: signature(x = "EMM"): Make a copy of the EMM object. Making explicit copies is necessary since the subclasses store information in environments which are not copied for regular assignements.
size: signature(x = "EMM"): Returns the size of the EMM (number of clusters/states).

References

M.H. Dunham, Y. Meng, J. Huang (2004): Extensible Markov Model, In: ICDM '04: Proceedings of the Fourth IEEE International Conference on Data Mining, pp. 371–374.

Examples

## load EMMTraffic data
data(EMMTraffic)

## create empty EMM
emm <- EMM(threshold=0.2, measure="eJaccard", lambda=.1)
emm
#> EMM with 0 states/clusters.
#>  Measure: eJaccard 
#>  Threshold: 0.2 
#>  Centroid: FALSE 
#>  Lambda: 0.1 

## cluster some data
build(emm, EMMTraffic)
emm
#> EMM with 7 states/clusters.
#>  Measure: eJaccard 
#>  Threshold: 0.2 
#>  Centroid: FALSE 
#>  Lambda: 0.1 

## what clusters were the data points assigned to?
last_clustering(emm)
#>  [1] "1" "2" "1" "2" "3" "4" "5" "2" "6" "4" "5" "7"

## plot the clustering as a graph
plot(emm)

Class "EMM"