{"type":"ScholarlyArticle","title":"Non-Euclidean Self-Organizing Maps","authors":[{"name":"Dorota Celińska-Kopczyńska","slug":"dorota-celinska-kopczynska","given":"Dorota","family":"Celińska-Kopczyńska","affiliation":"Institute of Informatics , University of Warsaw"},{"name":"Eryk Kopczyński","slug":"eryk-kopczynski","given":"Eryk","family":"Kopczyński","affiliation":"Institute of Informatics , University of Warsaw"}],"abstract":"Self-Organizing Maps (SOMs, Kohonen networks) belong to neural network models of the unsupervised class. In this paper, we present the generalized setup for non-Euclidean SOMs. Most data analysts take it for granted to use some subregions of a flat space as their data model; however, by the assumption that the underlying geometry is non-Euclidean we obtain a new degree of freedom for the techniques that translate the similarities into spatial neighborhood relationships. We improve the traditional SOM algorithm by introducing topology-related extensions. Our proposition can be successfully applied to dimension reduction, clustering or finding similarities in big data (both hierarchical and non-hierarchical).","doi":"10.24963/ijcai.2022/269","url":"https://ijcai.org/proceedings/2022/269","pdf":"https://ijcai.org/proceedings/2022/0269.pdf","markdown":"https://ijcai.org/proceedings/2022/269.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/269","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Data Mining","track":"Main Track","number":269,"pages":{"first":1938,"last":1944},"keywords":["Machine Learning: Feature Extraction, Selection and Dimensionality Reduction","Data Mining: Data Visualisation","Data Mining: Other","Data Mining: Exploratory Data Mining"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986470","length_sec":90,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984613","length_sec":323,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Asan and Ercan, 2012] Umut Asan and Secil Ercan. 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