SubClu: The SubClu Algorithm for Subspace Clustering

Description Usage Arguments References See Also Examples

View source: R/subclu.R

Description

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.

Usage

1
SubClu(data, epsilon = 4, minSupport = 4)

Arguments

data

A Matrix of input data.

epsilon

size of environment parameter for DBSCAN

minSupport

minimum number of points parameter for DBSCAN

References

Karin Kailing, Hans-Peter Kriegel and Peer Kr<c3><b6>ger Density-Connected Subspace Clustering for High-Dimensional Data

See Also

Other subspace.clustering.algorithms: CLIQUE; FIRES; P3C; ProClus

Examples

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

subspace documentation built on May 20, 2017, 12:42 a.m.

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