Interpretable clustering: an optimization approach

Author:

Bertsimas DimitrisORCID,Orfanoudaki Agni,Wiberg Holly

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference47 articles.

1. Arthur, D., & Vassilvitskii, S. (2007). k-means++: The advantages of careful seeding. In Proceedings of the 18th annual ACM-SIAM symposium on Discrete algorithms (pp 1027–1035). Society for Industrial and Applied Mathematics.

2. Basak, J., & Krishnapuram, R. (2005). Interpretable hierarchical clustering by constructing an unsupervised decision tree. IEEE Transactions on Knowledge and Data Engineering, 17, 121–132. https://doi.org/10.1109/TKDE.2005.11.

3. Bertsimas, D., & Dunn, J. (2017). Optimal classification trees. Machine Learning, 106(7), 1039–1082.

4. Bertsimas, D., & Dunn, J. (2019). Machine Learning under a Modern Optimization Lens. Waltham: Dynamic Ideas Press.

5. Bertsimas, D., O’Hair, A. K., & Pulleybank, W. R. (2016). The Analytics Edge. Waltham: Dynamic Ideas Press.

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