Exact weighted cluster editing

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

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.

Value

An integer vector representing the cluster affiliation of each data point

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. The function uses a "solver" to optimize the clustering objective. See optimal_anticlustering for an overview of the solvers that are available.

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.

Author

Martin Papenberg martin.papenberg@hhu.de

Examples

# \donttest{
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)

# }