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Clustering with Deep Learning: Taxonomy and New Methods
Elie Aljalbout, Vladimir Golkov, Yawar Siddiqui, Maximilian Strobel, Daniel Cremers
TL;DR
Deep clustering methods need representations that accommodate dataset-specific separation requirements. This paper organizes them into a taxonomy, validates systematic component recombination in a case study, and reports state-of-the-art quality, including surpassing prior methods in some cases.
Problem
Clustering methods depend on dataset-specific representations, similarity measures, and separation techniques, motivating methods that learn clustering-friendly feature spaces.
Method
The paper builds a taxonomy from existing deep-neural-network clustering methods and uses it to design a case-study method by selectively recombining or replacing distinct building blocks.
Results
The case-study method surpasses the state of the art for NMI on MNIST with a value of 0.923 and performs comparably on other reported comparisons.
Takeaways & Limitations
The taxonomy supports systematic and analytical creation of high-performing clustering methods suited to researchers' and practitioners' tasks.
Takeaways & Limitations
Phases omitting either the non-clustering loss or clustering loss can have potential disadvantages.
Abstract
from arXiv · showhide
Clustering methods based on deep neural networks have proven promising for clustering real-world data because of their high representational power. In this paper, we propose a systematic taxonomy of clustering methods that utilize deep neural networks. We base our taxonomy on a comprehensive review of recent work and validate the taxonomy in a case study. In this case study, we show that the taxonomy enables researchers and practitioners to systematically create new clustering methods by selectively recombining and replacing distinct aspects of previous methods with the goal of overcoming their individual limitations. The experimental evaluation confirms this and shows that the method created for the case study achieves state-of-the-art clustering quality and surpasses it in some cases.
1 INTRODUCTION
Clustering performance depends strongly on the input data, motivating deep neural networks that learn clustering-friendly representations. The paper proposes a taxonomy for systematically analyzing and designing such methods, illustrated through a case-study method.
- Clustering separates similar data points into groups and supports applications in data analysis and visualization.
- Different datasets require different similarity measures and separation techniques, making clustering performance highly dependent on input data.
- Deep neural networks learn non-linear mappings that transform data into clustering-friendly representations without manual feature extraction or selection.
- The paper formulates a taxonomy of deep-neural-network clustering methods and uses it to recombine or replace method components systematically.The taxonomy is intended to help researchers and practitioners improve performance, mitigate limitations, and select building blocks for specific tasks.
- The case study combines a fully convolutional autoencoder with two-phased training, using reconstruction loss first and a combined reconstruction-plus-clustering loss during fine-tuning.
2 TAXONOMY
The taxonomy decomposes deep neural network clustering methods into selectable components spanning representation learning, feature selection, loss functions, loss combination, cluster updates, and optional reclustering. These components include network architectures, deep-feature choices, non-clustering and clustering objectives, and assignment strategies.
- Taxonomy components: Each method combines a neural-network training procedure with an optional post-training reclustering step.The training procedure covers the main branch, clustering features, losses, loss combination, and cluster updates.
- Architecture: Main-branch architectures include MLPs, CNNs, DBNs, GANs, and VAEs, which transform inputs into latent representations for clustering.CNNs are described as useful for regular-grid data when locality and shift-equivariance or invariance are desired.
- Deep features: Deep features may come from one layer for lower dimensionality or several layers for richer semantic representations that can enhance separation and similarity computation.The selected features are taken from one or more network layers after the input is transformed into a clustering-friendly representation.
- Non-clustering losses: Non-clustering losses impose model constraints independently of the clustering algorithm, including reconstruction, self-augmentation, and auxiliary-task objectives.Autoencoder reconstruction preserves important input information, while self-augmentation brings representations of original samples and augmentations closer.
- Clustering losses: Clustering losses directly shape clustering-friendly representations through options such as no clustering loss, k-means loss, assignment hardening, balanced assignments, locality preservation, and group sparsity.Assignment hardening sharpens soft probabilities using an auxiliary distribution, while balanced-assignment loss targets uniform cluster assignments unless a known prior replaces it.
- Specialized objectives: Group sparsity uses divided hidden units and a group-based representation, whereas locality-preserving loss pushes nearby data points together.The group-sparsity formulation uses the assumed number of clusters as the number of hidden-unit groups.
3 RELATED METHODS
The taxonomy decomposes deep clustering methods into building blocks and organizes prior approaches by architectures, losses, features, and clustering procedures. Reviewed methods include convolutional and autoencoder-based feature extractors, alternative deep architectures, and neural ensemble clustering.
- Table 1 decomposes existing methods by network architecture, features, non-clustering loss, clustering loss, loss combination, clustering algorithm, and performance.
- Convolutional approaches use neural-network outputs or internal layers as features, with methods differing in clustering loss, initialization, and pretraining.
- Autoencoder methods commonly pretrain with reconstruction loss, then differ in whether they use cluster hardening, joint k-means and reconstruction, other losses, or no second phase.
- Other approaches use deep belief networks, GAN discriminator features, variational autoencoders with Gaussian mixtures, or neural networks that fuse ensemble partitions.
- The proposed method is described as a fully convolutional autoencoder trained with reconstruction and cluster hardening losses to produce clustering-friendly features without collapse risk.
4 CASE STUDY: NEW METHOD
The case study uses the taxonomy to design a new image-clustering method by selecting and recombining building blocks. It combines a fully convolutional autoencoder with reconstruction preservation, cluster hardening, and post-training k-means.
- The case study systematically recombines taxonomy building blocks to address limitations of previous approaches and improve clustering results.
- The design retains reconstruction loss while adding cluster hardening because omitting reconstruction can risk cluster collapse and worse clustering quality.
- After both phases, the encoder produces a clustering-friendly representation, and k-means is rerun on that representation for final clustering.
5 EXPERIMENTAL RESULTS
Experiments evaluate the proposed method on MNIST and COIL20 using ACC and NMI, with known labels withheld from the methods. The method improves the learned clustering spaces, surpasses the MNIST NMI state of the art, and delivers balanced results across datasets and metrics.
- Experimental setup: Experiments compare the proposed method with prior methods on two publicly available image datasets, MNIST and COIL20.MNIST contains 70,000 handwritten-digit images; COIL20 contains 1,440 grayscale images of 20 objects.
- Validation metrics: Evaluation uses clustering accuracy (ACC) and normalized mutual information (NMI), both ranging from 0 to 1 with 1 indicating perfect clustering.
- Performance: 0.923 NMI on MNIST surpasses the state of the art, while performance is comparable on the other dataset-metric combinations.
- Performance: The case-study method achieves balanced quality across datasets and enables high-performing methods to be created through structured, analytical design.
- Visualization results: The proposed method produces more clustering-friendly spaces than original image space and the pretrained autoencoder space before joint clustering-loss training.The visualizations cover 120-dimensional MNIST and 320-dimensional COIL20 clustering spaces.
6 CONCLUSION
The paper presents a universal taxonomy for deep-neural-network clustering and validates it through a case study that derives a new method systematically. That method outperforms the previous MNIST state of the art, supporting the taxonomy’s use for designing task-specific methods.
- The taxonomy decomposes existing deep clustering methods into building blocks and analyzes which properties indicate desirable or undesirable results.
- The case study shows that researchers and practitioners can use the taxonomy to derive new clustering methods systematically for their tasks.
- The constructed method outperforms the previous state-of-the-art method on MNIST, motivating further methods derived through the same approach.