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t-SNE and hierarchical clustering are popular methods of exploratory data analysis, particularly in biology.
On the evolution of random graphs
Erdös, P. & Rényi, A · 1960
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A database for handwritten text recognition research
Hull, J · 1994
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A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise
Ester, M. et al · 1996
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Columbia Object Image Library (COIL-20)
Nene, S.A., Nayar, S.K. & Murase, H · 1996
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Columbia Object Image Library (COIL-100)
Nene, S.A., Nayar, S.K. & Murase, H · 1996
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P · 1998
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Using the Nyström method to speed up kernel machines
Williams, C. K. I. & Seeger, M · 2000
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A new shared nearest neighbor clustering algorithm and its applications
Ertöz, L., Steinbach, M. & Kumar, V · 2002
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Stochastic neighbor embedding
Hinton, G. & Roweis, S · 2002
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Cluster Ensembles – A Knowledge Reuse Framework for Combining Multiple Partitions
Strehl, A. & Ghosh, J · 2002
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A tutorial on spectral clustering
von Luxburg, U · 2007
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Fast unfolding of communities in large networks
Blondel, V.D., Guillaume, J-L., Lambiotte, R. & Lefebvre, E · 2008
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Visualizing data using t t -SNE
van der Maaten, L. & Hinton, G · 2008
Cited alongside, same era.
Learning a Parametric Embedding by Preserving Local Structure
van der Maaten, L · 2009
Cited alongside, same era.
Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum
Bendall, S.C. et al · 2011
Cited alongside, same era.
Auto-encoder based data clustering
Song, C. et al · 2013
Cited alongside, same era.
Agglomerative clustering via maximum incremental path integral
Zhang, W., Zhao, D. & Wang, X · 2013
Cited alongside, same era.
A Deep Semi-NMF Model for Learning Hidden Representations
Trigeorgis, G., Bousmalis, K., Zafeiriou, S. & Schuller, B · 2014
Cited alongside, same era.
Deep clustering with convolutional autoencoders
Guo, X., Liu, X., Zhu, E. & Yin, J · 2017
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Efficient algorithms for t-distributed stochastic neighborhood embedding
Linderman, G. C., Rachh, M., Hoskins, J. G., Steinerberger, S. & Kluger, Y · 2017
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Improving clustering performance using independent component analysis and unsupervised feature learning
Gultepe, E. and Makrehchi, M · 2018
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Discriminatively boosted image clustering with fully convolutional auto-encoders
Li, F., Qiao, H. & Zhang, B · 2018
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SpectralNet: spectral clustering using deep neural networks
Shaham, U. et al · 2018
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Different Subsets of T Cells, Memory, Effector Functions, and CAR-T Immunotherapy
Golubovskaya, V. & Wu, L · 2016
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Infinite ensemble for image clustering
Liu, H., Shao, M., Li, S. & Fu, Y · 2016
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Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics
Shekhar, K. et al · 2016
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Learning a task-specific deep architecture for clustering
Wang, Z. et al · 2016
Cited alongside, same era.
Joint Unsupervised Learning of Deep Representations and Image Clusters
Yang, J., Parikh, D. & Batra, D · 2016
Cited alongside, same era.
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization
Dizaji, K. G. et al · 2017
Cited alongside, same era.
The art of using t-SNE for single-cell transcriptomics
Kobak, D. & Berens, P · 2019
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Heavy-tailed kernels reveal a finer cluster structure in t t -SNE visualisations
Kobak, D., Linderman, G., Steinerberger, S., Kluger, Y. & Berens, P · 2019
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Fast interpolation-based t t -SNE for improved visualization of single-cell RNA-seq data
Linderman, G. C., Rachh, M., Hoskins, J. G., Steinerberger, S. & Kluger, Y · 2019
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Clustering with t t -SNE, provably
Linderman, G. C. & Steinerberger, S · 2019
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Visualizing structure and transitions in high-dimensional biological data
Moon, K.R. et al · 2019
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Lecture, Math 421a: The Mathematics of Data Science
Steinerberger, S · 2019
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How to tune hyperparameters of tSNE [blog post]
Oskolkov, N · 2020
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