Kmeans Macro-clusterer

Macro Clusterer. Class implements the k-means algorithm for reclustering a set of micro-clusters.

Usage

DSC_Kmeans(
  formula = NULL,
  k,
  weighted = TRUE,
  iter.max = 10,
  nstart = 10,
  algorithm = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"),
  min_weight = NULL,
  description = NULL
)

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.
k: either the number of clusters, say k, or a set of initial (distinct) cluster centers. If a number, a random set of (distinct) rows in x is chosen as the initial centers.
weighted: use a weighted k-means (algorithm is ignored).
iter.max: the maximum number of iterations allowed.
nstart: if centers is a number, how many random sets should be chosen?
algorithm: character: may be abbreviated.
min_weight: micro-clusters with a weight less than this will be ignored for reclustering.
description: optional character string to describe the clustering method.

Value

An object of class DSC_Kmeans (subclass of DSC, DSC_R, DSC_Macro)

Details

update() and recluster() invisibly return the assignment of the data points to clusters.

Please refer to function stats::kmeans() for more details on the algorithm.

Note that this clustering cannot be updated iteratively and every time it is used for (re)clustering, the old clustering is deleted.

Author

Michael Hahsler

Examples

# 3 clusters with 5% noise
stream <- DSD_Gaussians(k = 3, d = 2, noise = 0.05)

# Use a moving window for "micro-clusters and recluster with k-means (macro-clusters)
cl <- DSC_TwoStage(
  micro = DSC_Window(horizon = 100),
  macro = DSC_Kmeans(k = 3)
)

update(cl, stream, 500)
cl
#> Sliding window + k-Means (weighted) 
#> Class: DSC_TwoStage, DSC_Macro, DSC 
#> Number of micro-clusters: 100 
#> Number of macro-clusters: 3 

plot(cl, stream)