ccls {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.
ccls(data, k = -1, mustLink, cantLink, maxIter = 1, tabuIter = 100, tabuLength = 20)
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 |
tabuIter |
Number of iteration in Tabu search |
tabuLength |
The number of elements in the Tabu list |
This algorithm minimizes the clustering cost function using Tabu search.
A vector that represents the labels (clusters) of the data points
This is the first algorithm for pairwise constrained clustering by local search.
Tran Khanh Hiep Nguyen Minh Duc
Tran Khanh Hiep, Nguyen Minh Duc, Bui Quoc Trung (2016), Pairwise Constrained Clustering by Local Search.
Tran Khanh Hiep, Nguyen Minh Duc, Bui Quoc Trung (2016), Pairwise Constrained Clustering by Local Search.
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