Description Usage Arguments References See Also Examples

The CLIQUE Algorithm finds clusters by first dividing each dimension into xi equal-width intervals and saving those intervals where the density is greater than tau as clusters. Then each set of two dimensions is examined: If there are two intersecting intervals in these two dimensions and the density in the intersection of these intervals is greater than tau, the intersection is again saved as a cluster. This is repeated for all sets of three, four, five,... dimensions. After every step adjacent clusters are replaced by a joint cluster and in the end all of the clusters are output.

1 |

`data` |
A Matrix of input data. |

`xi` |
Number of Intervals. |

`tau` |
Density Threshold. |

Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos, and
Prabhakar Raghavan. *Automatic Subspace Clustering of High Dimensional
Data for Data Mining Applications*. In Proc. ACM SIGMOD, 1999.

Other subspace.clustering.algorithms: `FIRES`

;
`P3C`

; `ProClus`

;
`SubClu`

1 2 | ```
data("subspace_dataset")
CLIQUE(subspace_dataset,xi=40,tau=0.06)
``` |

subspace documentation built on May 30, 2017, 2:39 a.m.

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