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Deep clustering has emerged as an important unsupervised category, aiming to exploit the non-linear mapping capabilities of neural networks in order to enhance clustering performance. The majority of deep clustering literature focuses on minimizing the inner-cluster variability in some embedded space while keeping the learned representation consistent with the original high-dimensional dataset. In this work, we propose\n                    <jats:italic>soft silhouette<\/jats:italic>\n                    , a probabilistic formulation of the silhouette coefficient. Soft silhouette rewards compact and distinctly separated clustering solutions such as the conventional silhouette coefficient. When optimized within a deep clustering framework, soft silhouette guides the learned representations towards forming compact and well-separated clusters. In addition, we introduce an autoencoder-based deep learning architecture that is suitable for optimizing the soft silhouette objective function. The proposed deep clustering method has been tested and compared with several well-studied deep clustering methods on various benchmark datasets, yielding very satisfactory clustering results.\n                  <\/jats:p>","DOI":"10.1007\/s10994-026-07026-w","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T15:23:38Z","timestamp":1774970618000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusters"],"prefix":"10.1007","volume":"115","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1352-2062","authenticated-orcid":false,"given":"Georgios","family":"Vardakas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8668-4477","authenticated-orcid":false,"given":"Ioannis","family":"Papakostas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3170-5428","authenticated-orcid":false,"given":"Aristidis","family":"Likas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,31]]},"reference":[{"key":"7026_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107522","volume":"108","author":"S Affeldt","year":"2020","unstructured":"Affeldt, S., Labiod, L., & Nadif, M. 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