Abstract: We connect the problem of semi-supervised clustering to constrained Markov aggregation, i.e., the task of partitioning the state space of a Markov chain. We achieve this connection by considering every data point in the dataset as an element of the Markov chain’s state space, by defining the transition probabilities between states via similarities between corresponding data points, and by incorporating semi-supervision information as hard constraints in a Hartigan-style algorithm. The introduced Constrained Markov Clustering (CoMaC) is an extension of a recent informationtheoretic framework for (unsupervised) Markov aggregation to the semi-supervised case. Instantiating CoMaC for certain parameter settings further generalizes two previous information-theoretic objectives for unsupervised clustering. Our results indicate that CoMaC is competitive with the state-of-the-art. (https://arxiv.org/abs/2112.09397)
The code was implemented in python3 version == 3.8 with the following packages:
scipy==1.6.2
scikit-learn==0.24.1
pandas==1.2.4
numpy==1.20.1
pathlib2==2.3.5
matplotlib==3.3.4
An example usage of the sequential and annealing clustering algorithm is shown in \notebooks\CoMaC-demo.ipynb.