The SubClu Algorithm follows a bottom-up framework, in which one-dimensional clusters are generated with DBSCAN and then each cluster is expanded one dimension at a time into a dimension that is known to have a cluster that only differs in one dimension from this cluster. This expansion is done using DBSCAN with the same parameters that were used for the original DBSCAN that produced the clusters.

1 |

`data` |
A Matrix of input data. |

`epsilon` |
size of environment parameter for DBSCAN |

`minSupport` |
minimum number of points parameter for DBSCAN |

Karin Kailing, Hans-Peter Kriegel and Peer KrĂ¶ger
*Density-Connected Subspace Clustering for High-Dimensional Data*

Other subspace.clustering.algorithms: `CLIQUE`

;
`FIRES`

; `P3C`

;
`ProClus`

1 2 | ```
data("subspace_dataset")
SubClu(subspace_dataset,epsilon=1,minSupport=5)
``` |

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