awesome-interpretable-clustering

A curated list of paper, methods and libraries implemented in Python for interpretability in clustering.

Contents

• Research Papers
• Libraries
• Tutorials
  • Reading Content
  • Videos and Courses

Research Papers

• ExKMC - ExKMC: Expanding Explainable k-Means Clustering.
• ShallowTree - Shallow decision trees for explainable clustering.
• PARTREE - Interpretable Data Partitioning Through Tree-Based Clustering Methods.
• G2PC - Algorithm-Agnostic Explainability for Unsupervised Clustering.
• ICOT - Interpretable clustering: an optimization approach. Interpretable Clustering via Optimal Trees.
• FACT - Algorithm-Agnostic Feature Attributions for Clustering.
• FuzzyTree - Interpretable fuzzy clustering using unsupervised fuzzy decision trees.
• DTEC - DTEC: Decision tree-based evidential clustering for interpretable partition of uncertain data.
• CLASSIX - CLASSIX: Fast and explainable clustering based on sorting.
• EXACT - ExACT Explainable Clustering: Unravelling the Intricacies of Cluster Formation.
• DTClust - Using Decision Trees for Interpretable Supervised Clustering.

Libraries

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• ExKMC - ExKMC: Expanding Explainable k-Means Clustering.
• ShallowTree - Shallow decision trees for explainable clustering.
• PARTREE - Interpretable Data Partitioning Through Tree-Based Clustering Methods.
• G2PC - Algorithm-Agnostic Explainability for Unsupervised Clustering.
• FACT - Algorithm-Agnostic Feature Attributions for Clustering.
• DTEC - DTEC: Decision tree-based evidential clustering for interpretable partition of uncertain data.
• CLASSIX - CLASSIX: Fast and explainable clustering based on sorting.
• EXACT - ExACT Explainable Clustering: Unravelling the Intricacies of Cluster Formation.

Tutorials

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Reading Content

• Convert sparse tabular data to a condensed representation in an image format

Videos and Online Courses

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• Neural Networks for NLP - Carnegie Mellon Language Technology Institute there

License

MIT