ckmeans {conclust} | R Documentation |
This function takes an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output.
ckmeans(data, k, mustLink, cantLink, maxIter = 100)
data |
The unlabeled dataset. |
k |
Number of clusters. |
mustLink |
A list of must-link constraints |
cantLink |
A list of cannot-link constraints |
maxIter |
Number of iteration |
This algorithm produces a clustering that satisfies all given constraints.
A vector that represents the labels (clusters) of the data points
The constraints should be consistent in order for the algorithm to work.
Tran Khanh Hiep Nguyen Minh Duc
Wagstaff, Cardie, Rogers, Schrodl (2001), Constrained K-means Clustering with Background Knowledge
Wagstaff, Cardie, Rogers, Schrodl (2001), Constrained K-means Clustering with Background Knowledge
data = matrix(c(0, 1, 1, 0, 0, 0, 1, 1), nrow = 4)
mustLink = matrix(c(1, 2), nrow = 1)
cantLink = matrix(c(1, 4), nrow = 1)
k = 2
pred = ckmeans(data, k, mustLink, cantLink)
pred