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Recent work on explainable clustering allows describing clusters when the features are interpretable.
Unsupervised classifiers, mutual information and’phantom targets
John S Bridle, Anthony JR Heading, and David JC MacKay · 1992
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Impact of similarity measures on web-page clustering
Alexander Strehl, Joydeep Ghosh, and Raymond Mooney · 2000
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Constrained k-means clustering with background knowledge
Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan Schrödl, et al · 2001
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Document clustering based on non-negative matrix factorization
Wei Xu, Xin Liu, and Yihong Gong · 2003
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Multi-view clustering
S Bickel and T Scheffer · 2004
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Integrating constraints and metric learning in semi-supervised clustering
Mikhail Bilenko, Sugato Basu, and Raymond J Mooney · 2004
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Clustering via decision tree construction
Bing Liu, Yiyuan Xia, and Philip S Yu · 2005
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Constrained clustering: Advances in algorithms, theory, and applications
Sugato Basu, Ian Davidson, and Kiri Wagstaff · 2008
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Describing objects by their attributes
Ali Farhadi, Ian Endres, Derek Hoiem, and David Forsyth · 2009
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Discriminative clustering by regularized information maximization
Andreas Krause, Pietro Perona, and Ryan Gomes · 2010
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Interpretable clustering using unsupervised binary trees
Ricardo Fraiman, Badih Ghattas, and Marcela Svarc · 2013
Cited alongside, same era.
Attribute-based classification for zero-shot visual object categorization
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2013
Cited alongside, same era.
A survey on multi-view learning
Chang Xu, Dacheng Tao, and Chao Xu · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Cited alongside, same era.
Multiple incomplete views clustering via weighted nonnegative matrix factorization with
Weixiang Shao, Lifang He, and S Yu Philip · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Peeking inside the black-box: A survey on explainable artificial intelligence (xai)
Amina Adadi and Mohammed Berrada · 2018
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Descriptive clustering: Ilp and cp formulations with applications
Thi-Bich-Hanh Dao, Chia-Tung Kuo, SS Ravi, Christel Vrain, and Ian Davidson · 2018
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The cluster description problem-complexity results, formulations and approximations
Ian Davidson, Antoine Gourru, and S Ravi · 2018
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Doubly aligned incomplete multi-view clustering
Menglei Hu and Songcan Chen · 2018
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Multiview spectral clustering via structured low-rank matrix factorization
Yang Wang, Lin Wu, Xuemin Lin, and Junbin Gao · 2018
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Clustering-driven deep embedding with pairwise constraints
Sharon Fogel, Hadar Averbuch-Elor, Daniel Cohen-Or, and Jacob Goldberger · 2019
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Cited alongside, same era.
” why should i trust you?” explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
Incomplete multi-modal visual data grouping
Handong Zhao, Hongfu Liu, and Yun Fu · 2016
Cited alongside, same era.
Clustering nominal data using unsupervised binary decision trees: Comparisons with the state of the art methods
Badih Ghattas, Pierre Michel, and Laurent Boyer · 2017
Cited alongside, same era.
From ensemble clustering to multi-view clustering
Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding, and Yun Fu · 2017
Cited alongside, same era.
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A framework for deep constrained clustering-algorithms and advances
Hongjing Zhang, Sugato Basu, and Ian Davidson · 2019
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Interpretable clustering: an optimization approach
Dimitris Bertsimas, Agni Orfanoudaki, and Holly Wiberg · 2020
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Explainable k-means and k-medians clustering
Michal Moshkovitz, Sanjoy Dasgupta, Cyrus Rashtchian, and Nave Frost · 2020
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Efficient algorithms for generating provably near-optimal cluster descriptors for explainability
Prathyush Sambaturu, Aparna Gupta, Ian Davidson, S. S. Ravi, Anil Vullikanti, and Andrew Warren · 2020
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