Hierarchical Micro-Cluster Reclusterer

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.

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.

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.

See also

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

Author

Michael Hahsler

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
#> Sliding window + Hierarchical (single) 
#> Class: DSC_TwoStage, DSC_Macro, DSC 
#> Number of micro-clusters: 100 
#> Number of macro-clusters: 6 

plot(cl, stream)