DSC_Reachability {stream}

Reachability Micro-Cluster Reclusterer

Description

Macro Clusterer. Implementation of reachability clustering (based on DBSCAN's concept of reachability) to recluster a set of micro-clusters.

Usage

DSC_Reachability(
  formula = NULL,
  epsilon,
  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.

epsilon

radius of the epsilon-neighborhood.

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

Two micro-clusters are directly reachable if they are within each other's epsilon-neighborhood (i.e., the distance between the centers is less then epsilon). Two micro-clusters are reachable if they are connected by a chain of pairwise directly reachable micro-clusters. All mutually reachable micro-clusters are put in the same cluster.

Reachability uses internally DSC_Hierarchical with single link.

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

An object of class DSC_Reachability. The object contains the following items:

description

The name of the algorithm in the DSC object.

RObj

The underlying R object.

Author(s)

Michael Hahsler

References

Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Evangelos Simoudis, Jiawei Han, Usama M. Fayyad. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96). AAAI Press. pp. 226-231.

See Also

Other DSC_Macro: DSC_DBSCAN(), DSC_EA(), DSC_Hierarchical(), DSC_Kmeans(), DSC_Macro(), 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 DBSCAN (macro-clusters)
cl <- DSC_TwoStage(
  micro = DSC_Window(horizon = 100),
  macro = DSC_Reachability(eps = .05)
)

update(cl, stream, 500)
cl

plot(cl, stream)