DSC_Kmeans

0th

Percentile

Kmeans Macro-clusterer

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

Usage
DSC_Kmeans(k, weighted = TRUE, iter.max = 10, nstart = 1, algorithm = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"), min_weight = NULL, description=NULL)
Arguments
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.
Details

Please refer to function kmeans in stats 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.

Value

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

See Also

DSC, DSC_Macro

Aliases
  • DSC_Kmeans
Examples
stream <- DSD_Gaussians(k=3, noise=0)

# create micro-clusters via sampling
sample <- DSC_Sample(k=20)
update(sample, stream, 500)
sample
  
# recluster micro-clusters
kmeans <- DSC_Kmeans(k=3)
recluster(kmeans, sample)
plot(kmeans, stream, type="both")

# For comparison we use k-means directly to cluster data
# Note: k-means is not a data stream clustering algorithm
kmeans <- DSC_Kmeans(k=3)
update(kmeans, stream, 500)
plot(kmeans, stream)
Documentation reproduced from package stream, version 1.2-3, License: GPL-3

Community examples

Looks like there are no examples yet.