Class implements the CluStream cluster algorithm for data streams.
Defines the maximum number of micro-clusters used in CluStream
Defines the time window to be used in CluStream
Maximal boundary factor (=Kernel radius factor).
When deciding to add a new data point to a
micro-cluster, the maximum boundary is defined as a factor of
Number of macro-clusters to produce using weighted k-means.
This is an interface to the MOA implementation of CluStream.
k is specified, then CluStream applies a weighted k-means
algorithm for reclustering (see Examples section below).
An object of class
DSC_CluStream (subclass of
k is not
NULL then an object of
Michael Hahsler and John Forrest
Aggarwal CC, Han J, Wang J, Yu PS (2003). "A Framework for Clustering Evolving Data Streams." In "Proceedings of the International Conference on Very Large Data Bases (VLDB '03)," pp. 81-92.
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).
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# data with 3 clusters and 5% noise stream <- DSD_Gaussians(k=3, d=2, noise=.05) # cluster with CluStream clustream <- DSC_CluStream(m=50) update(clustream, stream, 500) clustream # plot micro-clusters plot(clustream, stream) # plot assignment area (micro-cluster radius) plot(clustream, stream, assignment=TRUE, weights=FALSE) # reclustering. Use weighted k-means for CluStream kmeans <- DSC_Kmeans(k=3, weighted=TRUE) recluster(kmeans, clustream) plot(kmeans, stream, type="both") # use k-means reclustering automatically by specifying k clustream <- DSC_CluStream(m=50, k=3) update(clustream, stream, 500) clustream plot(clustream, stream, type="both")
Loading required package: stream Loading required package: proxy Attaching package: 'proxy' The following objects are masked from 'package:stats': as.dist, dist The following object is masked from 'package:base': as.matrix Loading required package: rJava CluStream Class: DSC_CluStream, DSC_Micro, DSC_MOA, DSC Number of micro-clusters: 50 CluStream + k-Means (weighted) Class: DSC_TwoStage, DSC_Macro, DSC Number of micro-clusters: 50 Number of macro-clusters: 3
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