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There exists a wide spectrum of clustering algorithms that can be used for this purpose. However, their practical applications share a common post-clustering phase, which concerns expert-based interpretation and analysis of the obtained results. We argue that this can be the bottleneck in the process, especially in cases where domain knowledge exists prior to clustering. Such a situation requires not only a proper analysis of automatically discovered clusters but also conformance checking with existing knowledge. In this work, we present Knowledge Augmented Clustering (<jats:sc>KnAC<\/jats:sc>). Its main goal is to confront expert-based labelling with automated clustering for the sake of updating and refining the former. Our solution is not restricted to any existing clustering algorithm. Instead, <jats:sc>KnAC<\/jats:sc> can serve as an augmentation of an arbitrary clustering algorithm, making the approach robust and a model-agnostic improvement of any state-of-the-art clustering method. We demonstrate the feasibility of our method on artificially, reproducible examples and in a real life use case scenario. In both cases, we achieved better results than classic clustering algorithms without augmentation.<\/jats:p>","DOI":"10.1007\/s10489-022-04310-9","type":"journal-article","created":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T13:59:35Z","timestamp":1669298375000},"page":"15537-15560","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["KnAC: an approach for enhancing cluster analysis with background knowledge and explanations"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6350-8405","authenticated-orcid":false,"given":"Szymon","family":"Bobek","sequence":"first","affiliation":[]},{"given":"Micha\u0142","family":"Kuk","sequence":"additional","affiliation":[]},{"given":"Jakub","family":"Brzegowski","sequence":"additional","affiliation":[]},{"given":"Edyta","family":"Brzychczy","sequence":"additional","affiliation":[]},{"given":"Grzegorz J.","family":"Nalepa","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,23]]},"reference":[{"key":"4310_CR1","doi-asserted-by":"crossref","unstructured":"Acharya A, Hruschka ER, Ghosh J, Acharyya S (2011) C3e: A framework for combining ensembles of classifiers and clusterers. 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