CCET: towards customized explanation of clustering
Author:
Funder
Hefei University of Technology
Publisher
Springer Science and Business Media LLC
Subject
Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s00371-023-02958-z.pdf
Reference27 articles.
1. Madhulatha, T.S.: “An overview on clustering methods,” arXiv preprint arXiv:1205.1117, (2012)
2. Chen, J., Chang, Y., Hobbs, B., Castaldi, P., Cho, M., Silverman, E., Dy, J.: Interpretable clustering via discriminative rectangle mixture model. In: 2016 IEEE 16th international conference on data mining (ICDM), pp. 823–828, IEEE, (2016)
3. Zhao, Y., Liang, S., Ren, Z., Ma, J., Yilmaz, E., de Rijke, M.: Explainable user clustering in short text streams. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 155–164, (2016)
4. Blockeel, H., De Raedt, L., Ramon, J.: Top-down induction of clustering trees. arXiv preprint cs/0011032, (2000)
5. Loyola-Gonzalez, O., Gutierrez-Rodríguez, A.E., Medina-Pérez, M.A., Monroy, R., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Garcia-Borroto, M.: An explainable artificial intelligence model for clustering numerical databases. IEEE Access 8, 52370–52384 (2020)
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