wce {anticlust}

Exact weighted cluster editing

Description

Optimally solves weighted cluster editing (also known as »correlation clustering« or »clique partitioning problem«).

Usage

wce(x, solver = NULL)

Arguments

x

A N x N similarity matrix. Larger values indicate stronger agreement / similarity between a pair of data points

solver

Optional argument; if passed, has to be either "glpk" or "symphony". See details.

Details

Finds the clustering that maximizes the sum of pairwise similarities within clusters. In the input some similarities should be negative (indicating dissimilarity) because otherwise the maximum sum of similarities is obtained by simply joining all elements within a single big cluster. If the argument solver is not specified, the function will try to find the GLPK or SYMPHONY solver by itself (it prioritizes using SYMPHONY if both are available).

Value

An integer vector representing the cluster affiliation of each data point

Note

This function either requires the R package Rglpk and the GNU linear programming kit (<http://www.gnu.org/software/glpk/>) or the R package Rsymphony and the COIN-OR SYMPHONY solver libraries (<https://github.com/coin-or/SYMPHONY>).

Author(s)

Martin Papenberg [e-mail]

References

Bansal, N., Blum, A., & Chawla, S. (2004). Correlation clustering. Machine Learning, 56, 89–113.

Böcker, S., & Baumbach, J. (2013). Cluster editing. In Conference on Computability in Europe (pp. 33–44).

Grötschel, M., & Wakabayashi, Y. (1989). A cutting plane algorithm for a clustering problem. Mathematical Programming, 45, 59-96.

Wittkop, T., Emig, D., Lange, S., Rahmann, S., Albrecht, M., Morris, J. H., ..., Baumbach, J. (2010). Partitioning biological data with transitivity clustering. Nature Methods, 7, 419–420.

Examples

features <- swiss
distances <- dist(scale(swiss))
hist(distances)
# Define agreement as being close enough to each other.
# By defining low agreement as -1 and high agreement as +1, we
# solve *unweighted* cluster editing
agreements <- ifelse(as.matrix(distances) < 3, 1, -1)
clusters <- wce(agreements)
plot(swiss, col = clusters, pch = 19)