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. 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).
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>).
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
# \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)
# }