Class implements the CluStream cluster algorithm for data streams.

1 | ```
DSC_CluStream(m = 100, horizon = 1000, t = 2, k=NULL)
``` |

`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 (=Kernel radius factor).
When deciding to add a new data point to a
micro-cluster, the maximum boundary is defined as a factor of |

`k` |
Number of macro-clusters to produce using weighted k-means. |

This is an interface to the MOA implementation of CluStream.

CluStream applies a weighted k-means algorithm for reclustering (see Examples section below).

An object of class `DSC_CluStream`

(subclass of
`DSC_Micro`

, `DSC_MOA`

and `DSC`

), or,
if `k`

is not `NULL`

then an object of `DSC_TwoStage`

.

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).

`DSC`

,
`DSC_Micro`

,
`DSC_MOA`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
# 3 clusters with 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 automatically
clustream <- DSC_CluStream(m=50, k=3)
update(clustream, stream, 500)
clustream
plot(clustream, stream, type="both")
``` |

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