ABSTRACT
The combination of clustering with Deep Learning has gained much attention in recent years. Unsupervised neural networks like autoencoders can autonomously learn the essential structures in a data set. This idea can be combined with clustering objectives to learn relevant features automatically. Unfortunately, they are often based on a k-means framework, from which they inherit various assumptions, like spherical-shaped clusters. Another assumption, also found in approaches outside the k-means-family, is knowing the number of clusters a-priori. In this paper, we present the novel clustering algorithm DipDECK, which can estimate the number of clusters simultaneously to improving a Deep Learning-based clustering objective. Additionally, we can cluster complex data sets without assuming only spherically shaped clusters. Our algorithm works by heavily overestimating the number of clusters in the embedded space of an autoencoder and, based on Hartigan's Dip-test - a statistical test for unimodality - analyses the resulting micro-clusters to determine which to merge. We show in extensive experiments the various benefits of our method: (1) we achieve competitive results while learning the clustering-friendly representation and number of clusters simultaneously; (2) our method is robust regarding parameters, stable in performance, and allows for more flexibility in the cluster shape; (3) we outperform relevant competitors in the estimation of the number of clusters.
Supplemental Material
- Horst Bischof, Alevs Leonardis, and Alexander Selb. 1999. MDL principle for robust vector quantisation. Pattern Analysis & Applications, Vol. 2, 1 (1999), 59--72.Google ScholarDigital Library
- Christian Böhm, Christos Faloutsos, Jia-Yu Pan, and Claudia Plant. 2006. Robust information-theoretic clustering. In SIGKDD. 65--75.Google Scholar
- Theofilos Chamalis and Aristidis Likas. 2018. The Projected Dip-Means Clustering Algorithm. In Proceedings of the 10th Hellenic Conference on Artificial Intelligence.Google ScholarDigital Library
- Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, and David Ha. 2018. Deep learning for classical Japanese literature. arXiv preprint arXiv:1812.01718 (2018).Google Scholar
- Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository. http://archive.ics.uci.edu/mlGoogle Scholar
- L. Duan, C. Aggarwal, S. Ma, and S. Sathe. 2019. Improving Spectral Clustering with Deep Embedding and Cluster Estimation. In ICDM. 170--179.Google Scholar
- Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, et almbox. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise.. In SIGKDD, Vol. 96. 226--231.Google ScholarDigital Library
- Yu Feng and Greg Hamerly. 2007. PG-means: learning the number of clusters in data. In Advances in neural information processing systems. 393--400.Google Scholar
- Kamran Ghasedi Dizaji, Amirhossein Herandi, Cheng Deng, Weidong Cai, and Heng Huang. 2017. Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization. In Proceedings of the IEEE international conference on computer vision. 5736--5745.Google ScholarCross Ref
- Xifeng Guo, Long Gao, Xinwang Liu, and Jianping Yin. 2017. Improved Deep Embedded Clustering with Local Structure Preservation. In IJCAI.Google Scholar
- Greg Hamerly and Charles Elkan. 2004. Learning the k in k-means. In Advances in neural information processing systems. 281--288.Google Scholar
- J. A. Hartigan and P. M. Hartigan. 1985. The Dip Test of Unimodality. Ann. Statist., Vol. 13, 1 (03 1985), 70--84. https://doi.org/10.1214/aos/1176346577Google Scholar
- Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, and Christian Igel. 2013. Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark. In IJCNN. IEEE, 1--8.Google Scholar
- Jonathan J. Hull. 1994. A database for handwritten text recognition research. IEEE Transactions on pattern analysis and machine intelligence, Vol. 16, 5 (1994), 550--554.Google ScholarDigital Library
- Zhuxi Jiang, Yin Zheng, Huachun Tan, Bangsheng Tang, and Hanning Zhou. 2017. Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering. In IJCAI. 1965--1972. https://doi.org/10.24963/ijcai.2017/273Google Scholar
- Argyris Kalogeratos and Aristidis Likas. 2012. Dip-means: an incremental clustering method for estimating the number of clusters. In Advances in Neural Information Processing Systems.Google Scholar
- Yann Lecun. 1987. PhD thesis: Modeles connexionnistes de l'apprentissage (connectionist learning models). Universite P. et M. Curie (Paris 6).Google Scholar
- Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE, Vol. 86, 11 (1998), 2278--2324.Google ScholarCross Ref
- Samuel Maurus and Claudia Plant. 2016. Skinny-dip: clustering in a sea of noise. In SIGKDD. 1055--1064.Google Scholar
- Dominik Mautz, Claudia Plant, and Christian Böhm. 2019. Deep embedded cluster tree. In ICDM. IEEE, 1258--1263.Google Scholar
- Tom Monnier, Thibault Groueix, and Mathieu Aubry. 2020. Deep Transformation-Invariant Clustering. In NeurIPS.Google Scholar
- Sudipto Mukherjee, Himanshu Asnani, Eugene Lin, and Sreeram Kannan. 2019. ClusterGAN: Latent Space Clustering in Generative Adversarial Networks. In AAAI. 4610--4617.Google Scholar
- Dan Pelleg and Andrew W. Moore. 2000. X-Means: Extending K-Means with Efficient Estimation of the Number of Clusters. In ICML (ICML '00).Google ScholarDigital Library
- B. Schelling, L. Bauer, S. Behzadi Soheil, and C. Plant. 2020. Utilizing Structure-rich Features to improve Clustering. In ECML-PKDD 2020.Google Scholar
- B. Schelling and C. Plant. 2018. Dip Transformation: Enhancing the Structure of a Dataset and Thereby Improving Clustering. In ICDM.Google Scholar
- Nguyen Xuan Vinh, Julien Epps, and James Bailey. 2010. Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance. Journal of Machine Learning Research (2010).Google Scholar
- Han Xiao, Kashif Rasul, and Roland Vollgraf. 2017. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. [arXiv]cs.LG/1708.07747 [cs.LG]Google Scholar
- Junyuan Xie, Ross B. Girshick, and Ali Farhadi. 2016. Unsupervised Deep Embedding for Clustering Analysis. In ICML (JMLR Workshop and Conference Proceedings).Google Scholar
- Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos, and Mingyi Hong. 2017. Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering. In ICML.Google Scholar
- Jianwei Yang, Devi Parikh, and Dhruv Batra. 2016. Joint Unsupervised Learning of Deep Representations and Image Clusters. In CVPR.Google Scholar
- Lihi Zelnik-Manor and Pietro Perona. 2005. Self-Tuning Spectral Clustering. In Advances in Neural Information Processing Systems.Google Scholar
Index Terms
- Dip-based Deep Embedded Clustering with k-Estimation
Recommendations
Centroids-guided deep multi-view K-means clustering
AbstractWith the progress of deep learning used in unsupervised learning, deep approach based multi-view clustering methods have been increasingly proposed in recent years. However, in most of these methods, deep representation learning is not ...
Point Symmetry-based Deep Clustering
CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge ManagementClustering is a central task in unsupervised learning. Recent advances that perform clustering into learned deep features (such as DEC[14], IDEC [6] or VaDe [10]) have shown improvements over classical algorithms, but most of them are based on the ...
Deep Convolutional Center-Based Clustering
Pattern Recognition and Computer VisionAbstractDeep clustering utilizes deep neural networks to learn feature representation which is suitable for clustering. One popular category of deep clustering algorithms combines stacked autoencoder and k-means clustering by defining objectives including ...
Comments