The Projected Dip-means Clustering Algorithm-Reference-Cited by-同舟云学术

The Projected Dip-means Clustering Algorithm

Published:2018-07-09 Issue: Volume: Page:
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Container-title:Proceedings of the 10th Hellenic Conference on Artificial Intelligence
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Author:

Chamalis Theofilos¹,Likas Aristidis¹

Affiliation:

1. Department of Computer Science & Engineering, University of Ioannina, GR, Ioannina, Greece

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3200947.3201008

Reference8 articles.

1. A. Asuncion and D.Newman. {n. d.}. UCI Machine Learning Repository. University of California at Irvine Irvine CA. ({n. d.}). http://www.ics.uci.edu/mlearn/MLRepository.html A. Asuncion and D.Newman. {n. d.}. UCI Machine Learning Repository. University of California at Irvine Irvine CA. ({n. d.}). http://www.ics.uci.edu/mlearn/MLRepository.html

2. G. Hamerly and C. Elkan. 2003. Learning the k in k-means. In Advances in Neural Information Processing Systems (NIPS '03). 281--288. G. Hamerly and C. Elkan. 2003. Learning the k in k-means. In Advances in Neural Information Processing Systems (NIPS '03). 281--288.

3. The Dip Test of Unimodality

4. Comparing partitions

5. A. Kalogeratos and A. Likas. 2012. Dip-means: an incremental clustering method for estimating the number of clusters. In Advances in Neural Information Processing Systems (NIPS '12). 2393--2401. A. Kalogeratos and A. Likas. 2012. Dip-means: an incremental clustering method for estimating the number of clusters. In Advances in Neural Information Processing Systems (NIPS '12). 2393--2401.

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