问题
I want to run some experiments on semi-supervised (constrained) clustering, in particular with background knowledge provided as instance level pairwise constraints (Must-Link or Cannot-Link constraints). I would like to know if there are any good open-source packages that implement semi-supervised clustering? I tried to look at PyBrain, mlpy, scikit and orange, and I couldn't find any constrained clustering algorithms. In particular, I'm interested in constrained K-Means or constrained density based clustering algorithms (like C-DBSCAN). Packages in Matlab, Python, Java or C++ would be preferred, but need not be limited to these languages.
回答1:
The python package scikit-learn has now algorithms for Ward hierarchical clustering (since 0.15) and agglomerative clustering (since 0.14) that support connectivity constraints.
Besides, I do have a real world application, namely the identification of tracks from cell positions, where each track can only contain one position from each time point.
回答2:
The R package conclust implements a number of algorithms:
There are 4 main functions in this package: ckmeans(), lcvqe(), mpckm() and ccls(). They take an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output.
There's also an implementation of COP-KMeans in python.
回答3:
Maybe its a bit late but have a look at the following.
An extension of Weka (in java) that implements PKM, MKM and PKMKM
http://www.cs.ucdavis.edu/~davidson/constrained-clustering/
Gaussian mixture model using EM and constraints in Matlab
http://www.scharp.org/thertz/code.html
I hope that this helps.
来源：https://stackoverflow.com/questions/21258367/what-are-some-packages-that-implement-semi-supervised-constrained-clustering