A framework for deep constrained clustering-Reference-Cited by-同舟云学术

A framework for deep constrained clustering

Published:2021-01-17 Issue:2 Volume:35 Page:593-620
ISSN:1384-5810
Container-title:Data Mining and Knowledge Discovery
language:en
Short-container-title:Data Min Knowl Disc

Author:

Zhang Hongjing^ORCID,Zhan Tianyang,Basu Sugato,Davidson Ian

Funder

Google

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://link.springer.com/content/pdf/10.1007/s10618-020-00734-4.pdf

Reference40 articles.

1. Aljalbout E, Golkov V, Siddiqui Y, Strobel M, Cremers D (2018) Clustering with deep learning: taxonomy and new methods. arXiv preprint arXiv:1801.07648

2. Bade K, Nürnberger A (2008) Creating a cluster hierarchy under constraints of a partially known hierarchy. In: Proceedings of the 2008 SIAM international conference on data mining. SIAM, pp 13–24

3. Basu S, Bilenko M, Mooney RJ (2004) A probabilistic framework for semi-supervised clustering. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 59–68

4. Basu S, Davidson I, Wagstaff K (2008) Constrained clustering: advances in algorithms, theory, and applications. CRC Press, Cambridge

5. Bilenko M, Basu S, Mooney RJ (2004) Integrating constraints and metric learning in semi-supervised clustering. In: Proceedings of the twenty-first international conference on machine learning. ACM, p 11

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