The CLIQUE Algorithm for Subspace Clustering

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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

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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

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