DSC_Hierarchical: Hierarchical Micro-Cluster Reclusterer

In stream: Infrastructure for Data Stream Mining

Hierarchical Micro-Cluster Reclusterer

Description

Macro Clusterer. Implementation of hierarchical clustering to recluster a set of micro-clusters.

Usage

DSC_Hierarchical(
  formula = NULL,
  k = NULL,
  h = NULL,
  method = "complete",
  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	The number of desired clusters.
h	Height where to cut the dendrogram.
method	the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward", "single", "complete", "⁠average"⁠, "mcquitty", "median" or "centroid".
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 hclust() for more details on the behavior of the algorithm.

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

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

Value

A list of class DSC, DSC_R, DSC_Macro, and DSC_Hierarchical. The list contains the following items:

description	The name of the algorithm in the DSC object.
RObj	The underlying R object.

Author(s)

Michael Hahsler

See Also

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

Examples

stream <- DSD_Gaussians(k = 3, d = 2, noise = 0.05)

# Use a moving window for "micro-clusters and recluster with HC (macro-clusters)
cl <- DSC_TwoStage(
  micro = DSC_Window(horizon = 100),
  macro = DSC_Hierarchical(h = .1, method = "single")
)

update(cl, stream, 500)
cl

plot(cl, stream)

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