DSC_Kmeans: Kmeans Macro-clusterer

In stream: Infrastructure for Data Stream Mining

Kmeans Macro-clusterer

Description

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.

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.

Value

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

Author(s)

Michael Hahsler

See Also

Other DSC_Macro: DSC_DBSCAN(), DSC_EA(), DSC_Hierarchical(), DSC_Macro(), DSC_Reachability(), DSC_SlidingWindow()

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

plot(cl, stream)

stream documentation built on May 29, 2024, 9:43 a.m.