This package provides access to five of the most popular clustering algorithms in the subspace paradigm.
FIRES can be applied to data.frames and matrices and will
return S3 objects representing clusterings. For example, using the built-in
demo dataset, you can do:
Subspace clustering generated by the package Subspace, containing 12
These subspace_clustering objects are actually just lists of subspace_cluster objects, which can be accessed as follows.
Subspace cluster generated by the package
Subspace. This cluster consists of 140 objects in a 3 dimensional subspace.
Each of these clusters then holds a vector representing its subspace and a
vector with the indexes of the objects the belong in this cluster. In this
example, these could be accessed as
This package also provides a
plot method for
visualisation. Press Escape/Ctrl + C to stop.
These plots are created using the
Finally, you can save clusterings to a file using the
For example you could save the clustering from this example to a file and load the true clustering of the demo dataset:
>path_to_clustering <- paste(path.package("subspace"),"/extdata/subspace_dataset.true",sep="")
true_clustering <- clustering_from_file(file_path=path_to_clustering)
Müller E., Günnemann S., Assent I., Seidl T.: Evaluating Clustering in Subspace Projections of High Dimensional Data http://dme.rwth-aachen.de/OpenSubspace/ In Proc. 35th International Conference on Very Large Data Bases (VLDB 2009), Lyon, France. (2009)