CLIQUE: The CLIQUE Algorithm for Subspace Clustering

In subspace: Interface to OpenSubspace

Description

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.

Usage

CLIQUE(data, xi = 10, tau = 0.2)

Arguments

data	A Matrix of input data.
xi	Number of Intervals.
tau	Density Threshold.

References

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.

See Also

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

Examples

data("subspace_dataset")
CLIQUE(subspace_dataset,xi=40,tau=0.06)

Example output

OpenJDK 64-Bit Server VM warning: Can't detect initial thread stack location - find_vma failed
Subspace clustering generated by the package Subspace, containing 16 clusters.Warning message:
system call failed: Cannot allocate memory 

subspace documentation built on May 2, 2019, 11:11 a.m.