lcvqe {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.
lcvqe(data, k, mustLink, cantLink, maxIter = 10)
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 finds a clustering that satisfies as many constraints as possible
A vector that represents the labels (clusters) of the data points
This algorithm can handle noisy constraints.
Tran Khanh Hiep Nguyen Minh Duc
Dan Pelleg, Dorit Baras (2007), K-means with large and noisy constraint sets
Dan Pelleg, Dorit Baras (2007), K-means with large and noisy constraint sets
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 = lcvqe(data, k, mustLink, cantLink)
pred