DSC_CluStream: CluStream Data Stream Clusterer

In mhahsler/streamMOA: Interface for MOA Stream Clustering Algorithms

CluStream Data Stream Clusterer

Description

Class implements the CluStream cluster algorithm for data streams (Aggarwal et al, 2003).

Usage

DSC_CluStream(m = 100, horizon = 1000, t = 2, k = 5)

Arguments

m	Defines the maximum number of micro-clusters used in CluStream
horizon	Defines the time window to be used in CluStream
t	Maximal boundary factor (i.e., the kernel radius factor). When deciding to add a new data point to a micro-cluster, the maximum boundary is defined as a factor of t of the RMS deviation of the data points in the micro-cluster from the centroid.
k	Number of macro-clusters to produce using weighted k-means.

Details

This is an interface to the MOA implementation of CluStream.

If k is specified, then CluStream applies a weighted k-means algorithm for reclustering (see Examples section below).

Value

An object of class DSC_CluStream (subclass of stream::DSC_Micro, DSC_MOA and stream::DSC).

Author(s)

Michael Hahsler and John Forrest

References

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).

See Also

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

Examples

# data with 3 clusters and 5% noise
set.seed(1000)
stream <- DSD_Gaussians(k = 3, d = 2, noise = .05)

# cluster with CluStream
clustream <- DSC_CluStream(m = 50, horizon = 100, k = 3)
update(clustream, stream, 500)
clustream

plot(clustream, stream, type = "both")

mhahsler/streamMOA documentation built on Aug. 30, 2024, 11:25 p.m.