DSC_DenStream: DenStream Data Stream Clusterer

Description

Class implements the DenStream cluster algorithm for data streams.

Usage

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

Arguments

epsilon

defines the epsilon neighbourhood which is the maximal radius of micro-clusters (r<=epsilon). Range: 0 to 1.

minpoints as the weight w a core-micro-clusters needs to be created (w>=mu). Range: 0 to max(int).

beta

multiplier for mu to detect outlier micro-clusters given their weight w (w<beta x mu). Range: 0 to 1

lambda

decay constant.

initPoints

number of points to use for initialization via DBSCAN.

offline

offline multiplier for epsilon. Range: between 2 and 20). Used for reachability reclustering

processingSpeed

Number of incoming points per time unit (important for decay). Range: between 1 and 1000.

recluster

logical; should the offline DBSCAN-based (i.e., reachability at a distance of epsilon) be performed?

integer; tries to automatically chooses offline to find k macro-clusters.

Value

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

Details

Interface to the DenStream implementation in MOA.

DenStream applies weighted DBSCAN for reclustering (see Examples section below).

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).

Examples

Run this code

# NOT RUN {
# 3 clusters with 5% noise
stream <- DSD_Gaussians(k=3, d=2, noise=0.05)

denstream <- DSC_DenStream(epsilon=.05)
update(denstream, stream, 500)
denstream

# plot micro-clusters
plot(denstream, stream)
  
# plot the micro-cluster assignment area
plot(denstream, stream, assignment=TRUE, weights=FALSE)

# show macro-clusters (using density reachability with epsilon x offline)
plot(denstream, stream, type="both")

# reclustering. DenStream micro-clusters with k-means instead
km <- DSC_Kmeans(k=3, weighted=TRUE)
recluster(km, denstream)
plot(km, stream, type="both")
# }

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