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DenStream Data Stream Clusterer

Source: R/DSC_DenStream.R
DSC_DenStream.Rd

Interface for the DenStream cluster algorithm for data streams implemented in MOA.

Usage

DSC_DenStream(
  epsilon,
  mu = 1,
  beta = 0.2,
  lambda = 0.001,
  initPoints = 100,
  offline = 2,
  processingSpeed = 1,
  recluster = TRUE,
  k = NULL
)

Arguments

epsilon

Maximum radius of a micro-cluster. Must be between 0 and 1.

mu

Minimum weight required for a core micro-cluster.

beta

Weight multiplier used to identify outlier micro-clusters. Must be between 0 and 1.

lambda

Decay constant.

initPoints

Number of points used to initialize the algorithm with DBSCAN.

offline

Multiplier applied to epsilon for reachability reclustering. Must be between 2 and 20.

processingSpeed

Number of incoming points per time unit, used for decay. Must be between 1 and 1000.

recluster

If TRUE, apply offline reachability reclustering.

k

If specified, choose a reachability threshold to produce this number of macro-clusters.

Value

An object of class DSC_DenStream (subclass of stream::DSC, DSC_MOA, stream::DSC_Micro) or, for recluster = TRUE, an object of class stream::DSC_TwoStage.

Details

DenStream reclusters micro-clusters using DBSCAN-style reachability. The threshold is epsilon * offline (with offline = 2 by default).

If k is specified, single-link hierarchical clustering chooses a reachability threshold that produces k macro-clusters.

References

Cao F, Ester M, Qian W, Zhou A (2006). Density-Based Clustering over an Evolving Data Stream with Noise. In Proceedings of the 2006 SIAM International Conference on Data Mining, pp 326-337. SIAM.

Bifet A, Holmes G, Pfahringer B, Kranen P, Kremer H, Jansen T, Seidl T (2010). MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering. In Journal of Machine Learning Research (JMLR).

See also

Other DSC_MOA: DSC_BICO_MOA(), DSC_CluStream(), DSC_ClusTree(), DSC_DStream_MOA(), DSC_MCOD(), DSC_MOA(), DSC_StreamKM()

Author

Michael Hahsler and John Forrest

Examples

# data with 3 clusters and 5% noise
set.seed(1000)
stream <- DSD_Gaussians(k = 3, d = 2, noise = 0.05)

# use Den-Stream with reachability reclustering
denstream <- DSC_DenStream(epsilon = .05)
update(denstream, stream, 500)
denstream
#> DenStream + Reachability 
#> Class: DSC_TwoStage, DSC_Macro, DSC 
#> Number of micro-clusters: 20 
#> Number of macro-clusters: 3 

# plot macro-clusters
plot(denstream, stream, type = "both")


# plot micro-clusters
plot(denstream, stream, type = "micro")


# reclustering: Choose reclustering reachability threshold automatically to find 4 clusters
denstream2 <- DSC_DenStream(epsilon = .05, k = 4)
update(denstream2, stream, 500)
plot(denstream2, stream, type = "both")