View source: R/DSC_DenStream.R
DSC_DenStream | R Documentation |
Interface for the DenStream cluster algorithm for data streams implemented in MOA.
DSC_DenStream( epsilon, mu = 1, beta = 0.2, lambda = 0.001, initPoints = 100, offline = 2, processingSpeed = 1, recluster = TRUE, k = NULL )
epsilon |
defines the epsilon neighbourhood which is the maximal radius of micro-clusters (r<=epsilon). Range: 0 to 1. |
mu |
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? |
k |
integer; tries to automatically chooses offline to find k macro-clusters. |
DenStream applies reachbility (from DBSCAN) between micro-clusters for
reclustering using epsilon
x offline
(defaults to 2) as the
reachability threshold.
If k
is specified it automatically chooses the reachability threshold
to find k clusters. This is achieved using single-link hierarchical
clustering.
An object of class DSC_DenStream
(subclass of DSC,
DSC_MOA, DSC_Micro) or, for recluster = TRUE
, an object
of class DSC_TwoStage.
Michael Hahsler and John Forrest
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).
Other DSC_MOA:
DSC_BICO_MOA()
,
DSC_CluStream()
,
DSC_ClusTree()
,
DSC_DStream_MOA()
,
DSC_MCOD()
,
DSC_MOA()
,
DSC_StreamKM()
# 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 # plot macro-clusters plot(denstream, stream, type = "both") # plot micro-cluster plot(denstream, stream, type = "micro") # show micro and macro-clusters plot(denstream, stream, type = "both") # 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")
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