mlsanityData to 14 Oct 2026

Unsupervised Deep Embedding for Clustering Analysis

arXiv 1511.06335 ↗First posted 19 Nov 2015cs.LG cs.CVcited by 767

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Abstract (arXiv)

Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has focused on learning representations for clustering. In this paper, we propose Deep Embedded Clustering (DEC), a method that simultaneously learns feature representations and cluster assignments using deep neural networks. DEC learns a mapping from the data space to a lower-dimensional feature space in which it iteratively optimizes a clustering objective. Our experimental evaluations on image and text corpora show significant improvement over state-of-the-art methods.

Key facts. Cited by 767 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Nov 2015.

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Citing papers per year: 2019: 2; 2020: 0; 2021: 164; 2022: 146; 2023: 152; 2024: 131; 2025: 98; 2026: 74.

767 papers.

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  37. 2021Joint Representation Learning and Novel Category Discovery on Single- and Multi-modal Datacited by 55
  38. 2022DeepDPM: Deep Clustering With an Unknown Number of Clusterscited by 52

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