Description Usage Arguments References See Also Examples

The ProClus algorithm works in a manner similar to K-Medoids. Initially, a set of medoids of a size that is proportional to k is chosen. Then medoids that are likely to be outliers or are part of a cluster that is better represented by another medoid are removed until k medoids are left. Clusters are then assumed to be around these medoids.

1 |

`data` |
A Matrix of input data. |

`k` |
Number of Clusters to be found. |

`d` |
Average number of dimensions in which the clusters reside |

C. C. Aggarwal and C. Procopiuc *Fast Algorithms for
Projected Clustering*. In Proc. ACM SIGMOD 1999.

Other subspace.clustering.algorithms: `CLIQUE`

;
`FIRES`

; `P3C`

;
`SubClu`

1 2 | ```
data("subspace_dataset")
ProClus(subspace_dataset,k=12,d=2.5)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.