specClust: Spectral Clustering

In kknn: Weighted k-Nearest Neighbors

Spectral Clustering

Description

Spectral clustering based on k-nearest neighbor graph.

Usage

specClust(data, centers = NULL, nn = 7, method = "symmetric", gmax = NULL, ...)

## S3 method for class 'specClust'
plot(x, ...)

Arguments

data	Matrix or data frame.
centers	number of clusters to estimate, if NULL the number is chosen automatically.
nn	Number of neighbors considered.
method	Normalisation of the Laplacian ("none", "symmetric" or "random-walk").
gmax	maximal number of connected components.
...	Further arguments passed to or from other methods.
x	an object of class specClust

Details

specClust allows to estimate several popular spectral clustering algorithms, for an overview see von Luxburg (2007).

The Laplacian is constructed from a from nearest neighbors and there are several kernels available. The eigenvalues and eigenvectors are computed using the binding in igraph to arpack. This should ensure that this algorithm is also feasible for larger datasets as the the the distances used have dimension n*m, where n is the number of observations and m the number of nearest neighbors. The Laplacian is sparse and has roughly n*m elements and only k eigenvectors are computed, where k is the number of centers.

Value

specClust returns a kmeans object or in case of k being a vector a list of kmeans objects.

Author(s)

Klaus P. Schliep [e-mail]

References

U. von Luxburg (2007) A tutorial on spectral clustering, Stat Comput, 17, 395–416

Ng, A., Jordan, M., Weiss, Y. (2002) On spectral clustering: analysis and an algorithm. In: Dietterich, T., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, 14, 849–856. MIT Press, Cambridge

Lihi Zelnik-Manor and P. Perona (2004) Self-Tuning Spectral Clustering, Eighteenth Annual Conference on Neural Information Processing Systems, (NIPS)

Shi, J. and Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (8), 888–905

See Also

kknn, arpack, kmeans

Examples

data(iris)
cl <- specClust(iris[,1:4], 3, nn=5)
pcol <- as.character(as.numeric(iris$Species))
pairs(iris[1:4], pch = pcol, col = c("green", "red", "blue")[cl$cluster])
table(iris[,5], cl$cluster)

kknn documentation built on April 16, 2025, 9:07 a.m.