Description Usage Arguments Details Author(s) References Examples

This is an interface to the MOA implementation of BICO. The original BICO implementation by Fichtenberger et al is also available as `DSC_BICO`

.

1 2 | ```
DSC_BICO_MOA(Cluster = 5, Dimensions, MaxClusterFeatures = 1000,
Projections = 10, k = NULL, space = NULL, p = NULL)
``` |

`Cluster, k` |
Number of desired centers |

`Dimensions` |
The number of the dimensions of the input points (stream) need to be specified in advance |

`MaxClusterFeatures, space` |
Maximum size of the coreset |

`Projections, p` |
Number of random projections used for the nearest neighbour search |

BICO maintains a tree which is inspired by the clustering tree of BIRCH, a SIGMOD Test of Time award-winning clustering algorithm. Each node in the tree represents a subset of these points. Instead of storing all points as individual objects, only the number of points, the sum and the squared sum of the subset's points are stored as key features of each subset. Points are inserted into exactly one node.

Matthias Carnein

Hendrik Fichtenberger, Marc Gille, Melanie Schmidt, Chris Schwiegelshohn, Christian Sohler: BICO: BIRCH Meets Coresets for k-Means Clustering. ESA 2013: 481-492

1 2 3 4 5 6 7 8 9 10 | ```
# data with 3 clusters and 2 dimensions
stream <- DSD_Gaussians(k=3, d=2)
# cluster with BICO
bico <- DSC_BICO_MOA(Cluster=3, Dimensions=2)
update(bico, stream, 10000)
bico
# plot micro and macro-clusters
plot(bico, stream, type="both")
``` |

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