stream (version 2.0-1)

DSC_Kmeans: 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
)

Value

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

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.

Author

Michael Hahsler

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.

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)