Explainability for Clustering Models-Reference-Cited by-同舟云学术

Explainability for Clustering Models

Published:2023 Issue: Volume: Page:3-17
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
Short-container-title:

Author:

Arora Mahima^ORCID,Chopra Ankush^ORCID

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-0405-1_1

Reference21 articles.

1. Hastie, T., Tibshirani, R.: Generalized additive models: some applications. J. Am. Stat. Assoc. 82(398), 371–386 (1987)

2. Müller, M.: Generalized linear models. In: Gentle, J., Härdle, W., Mori, Y. (eds.) Handbook of Computational Statistics. Springer Handbooks of Computational Statistics. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-21551-3_24

3. Ribeiro, M.T., Singh, S., Guestrin, C.: “why should I trust you?”: Explaining the predictions of any classifier. CoRR abs/1602.04938 (2016)

4. Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4–9, 2017, Long Beach, CA, USA, pp. 4765–4774 (2017)

5. Kanungo, T., et al.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002). https://doi.org/10.1109/TPAMI.2002.1017616

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. SVD-based Peephole and Clustering to Enhance Trustworthiness in DNN Classifiers;2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS);2024-04-22