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In this paper, we propose <jats:italic>A-Posteriori affinity Propagation<\/jats:italic> (APP), an incremental extension of affinity propagation (AP) based on <jats:italic>cluster consolidation<\/jats:italic> and <jats:italic>cluster stratification<\/jats:italic> to achieve faithfulness and forgetfulness. APP enforces incremental clustering where i) new arriving objects are dynamically consolidated into previous clusters without the need to re-execute clustering over the entire dataset of objects, and ii) a faithful sequence of clustering results is produced and maintained over time, while allowing to forget obsolete clusters with decremental learning functionalities. Four popular labeled datasets are used to test the performance of APP with respect to benchmark clustering performances obtained by conventional AP and incremental affinity propagation based on nearest neighbor assignment algorithms. Experimental results show that APP achieves comparable clustering performance while enforcing scalability at the same time.<\/jats:p>","DOI":"10.1007\/s11063-025-11752-y","type":"journal-article","created":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T21:29:10Z","timestamp":1745270950000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Incremental Affinity Propagation Based on Cluster Consolidation and Stratification"],"prefix":"10.1007","volume":"57","author":[{"given":"Francesco","family":"Periti","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefano","family":"Montanelli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alfio","family":"Ferrara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Silvana","family":"Castano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,21]]},"reference":[{"issue":"03","key":"11752_CR1","doi-asserted-by":"publisher","first-page":"2150038","DOI":"10.1142\/S0219649221500386","volume":"20","author":"I Arpaci","year":"2021","unstructured":"Arpaci I, Alshehabi S, Mahariq I, Topcu AE (2021) An evolutionary clustering analysis of social media content and global infection rates during the COVID-19 pandemic. 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