An Introduction to Deep Clustering

Nutakki, Gopi Chand; Abdollahi, Behnoush; Sun, Wenlong; Nasraoui, Olfa

doi:10.1007/978-3-319-97864-2_4

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…More recent developments in this area allow joint processes of deep learning and clustering, via optimizing a combined/joint loss function from both of them, an iterative process between them to improve model performance, or a self-supervised learning framework (Zhang et al, 2019). A detailed introduction of these methods can be found elsewhere (Karim et al, 2020;Nutakki et al, 2019).…”

Section: Neural Network and Deep-clusteringmentioning

confidence: 99%

Evaluating individual heterogeneity in mental health research: an overview of clustering methods and guidelines for applications

Gao¹,

Dwyer²,

Zhu³

et al. 2022

Preprint

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Clustering models or cluster analyses have been widely used to explore individual heterogeneity in mental health research. Despite advances in new algorithms and increasing popularity, there is little guidance on model choice, analytical framework and reporting requirements when using clustering models. In this review, we first provided a comprehensive introduction to the philosophy, design and implementation of major algorithms that are particularly relevant in mental health research. The design, comparisons and implementations (in R package) of different models and dissimilarity measures are discussed. The extensions of basic models, such as kernel method, deep learning, semi-supervised clustering, and clustering ensembles were subsequently introduced. Methods for pre-clustering data processing, clustering evaluation and validation, as well as important issues commonly faced in clustering tasks are discussed. Importantly, we provided general guidance on clustering workflow and reporting requirements. A rapid review of publications (December 2020-December 2021) was conducted focusing on the top six psychology and psychiatry journals that published most of the clustering papers. The results have highlighted that there was a lack of diversity in the algorithm of choice, robust validation processes via resampling, and available data and analysis code to improve reproducibility. This comprehensive review offers researchers advanced tools and guidelines to address some of these issues, improve practice and ultimately understanding of the complexity of mental illness.

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Section: Neural Network and Deep-clusteringmentioning

confidence: 99%

Evaluating individual heterogeneity in mental health research: an overview of clustering methods and guidelines for applications

Gao¹,

Dwyer²,

Zhu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Out of many deep learning methods, deep clustering is one approach that may be relevant for the topic at hand (for nice reviews of deep clustering, see [40][41][42][43][44][45], and references therein). deep clustering is a branch of unsupervised learning that combines traditional clustering methods with deep learning.…”

Section: Introductionmentioning

confidence: 99%

Datacube segmentation via deep spectral clustering

Bombini,

García-Avello Bofías,

Bracci

et al. 2024

Mach. Learn.: Sci. Technol.

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Extended Vision techniques are ubiquitous in physics. However, the data cubes steaming from such analysis often pose a challenge in their interpretation, due to the intrinsic difficulty in discerning the relevant information from the spectra composing the data cube.Furthermore, the huge dimensionality of data cube spectra poses a complex task in its statistical interpretation; nevertheless, this complexity contains a massive amount of statistical information that can be exploited in an unsupervised manner to outline some essential properties of the case study at hand, e.g. it is possible to obtain an image segmentation via (deep) clustering of data-cube's spectra, performed in a suitably defined low-dimensional embedding space.To tackle this topic, we explore the possibility of applying unsupervised clustering methods in encoded space, i.e. perform deep clustering on the spectral properties of datacube pixels. A statistical dimensional reduction is performed by an ad hoc trained (Variational) AutoEncoder, in charge of mapping spectra into lower dimensional metric spaces, while the clustering process is performed by a (learnable) iterative K-Means clustering algorithm.We apply this technique to two different use cases, of different physical origins: a set of Macro mapping X-Ray Fluorescence (MA-XRF) synthetic data on pictorial artworks, and a dataset of simulated astrophysical observations.

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“…On considering a purely unsupervised scenario, many approaches based on deep learning have been proposed recently. In that sense, [12] divides them into three different main strategies, as illustrated in Fig. 2.…”

Section: Introductionmentioning

confidence: 99%

Deep Categorization with Semi-Supervised Self-Organizing Maps

Braga

Medeiros

Bassani

2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Nowadays, with the advance of technology, there is an increasing amount of unstructured data being generated every day. However, it is a painful job to label and organize it. Labeling is an expensive, time-consuming, and difficult task. It is usually done manually, which collaborates with the incorporation of noise and errors to the data. Hence, it is of great importance to developing intelligent models that can benefit from both labeled and unlabeled data. Currently, works on unsupervised and semi-supervised learning are still being overshadowed by the successes of purely supervised learning. However, it is expected that they become far more important in the longer term. This article presents a semi-supervised model, called Batch Semi-Supervised Self-Organizing Map (Batch SS-SOM), which is an extension of a SOM incorporating some advances that came with the rise of Deep Learning, such as batch training. The results show that Batch SS-SOM is a good option for semisupervised classification and clustering. It performs well in terms of accuracy and clustering error, even with a small number of labeled samples, as well as when presented to unsupervised data, and shows competitive results in transfer learning scenarios in traditional image classification benchmark datasets.

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An Introduction to Deep Clustering

Cited by 10 publications

References 23 publications

Evaluating individual heterogeneity in mental health research: an overview of clustering methods and guidelines for applications

Evaluating individual heterogeneity in mental health research: an overview of clustering methods and guidelines for applications

Datacube segmentation via deep spectral clustering

Deep Categorization with Semi-Supervised Self-Organizing Maps

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