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We study constrained clustering, where constraints guide the clustering process.
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Z. Lu and T. K. Leen, “Penalized probabilistic clustering,” Neural Computation , 2007
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J. Peng and Y. Wei, “Approximating k-means-type clustering via semidefinite programming,” SIAM journal on optimization , 2007
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M. Meila, “Comparing clusterings–an information based distance,” Journal of multivariate analysis , vol. 98, no. 5, pp. 873–895, 2007
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F. Hoppner and F. Klawonn, “Clustering with size constraints,” in Computational Intelligence Paradigms . Springer, 2008, pp. 167–180
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Z. Lu and H. H. Ip, “Constrained spectral clustering via exhaustive and efficient constraint propagation,” in European Conference on Computer Vision . Springer, 2010, pp. 1–14
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H. Ding and J. Xu, “A unified framework for clustering constrained data without locality property,” in Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms . SIAM, 2015
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B. Wu and B. Ghanem, “lp-box admm: A versatile framework for integer programming,” IEEE transactions on pattern analysis and machine intelligence , 2018
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A. Alqahtani, X. Xie, J. Deng, and M. Jones, “A deep convolutional auto-encoder with embedded clustering,” in Proceedings of the IEEE International Conference on Image Processing , 2018, pp. 4058–4062
2018
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A. Alqahtani, X. Xie, J. Deng, and M. W. Jones, “Learning discriminatory deep clustering models,” in Proceedings of the International Conference on Computer Analysis of Images and Patterns , 2019, pp. 224–233
2019
Closest in time.
A. Alqahtani, M. Ali, X. Xie, and M. W. Jones, “Deep time-series clustering: A review,” Electronics , vol. 10, no. 23, p. 3001, 2021
2021
Closest in time.