Description Usage Arguments Details Value Note Author(s) References See Also Examples
This function takes an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output.
1 |
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 is one of the best algorithm for clustering with constraints.
Tran Khanh Hiep Nguyen Minh Duc
Bilenko, Basu, Mooney (2004), Integrating Constraints and Metric Learning in Semi-Supervised Clustering
Bilenko, Basu, Mooney (2004), Integrating Constraints and Metric Learning in Semi-Supervised Clustering
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