{"type":"ScholarlyArticle","title":"Mining Convex Polygon Patterns with Formal Concept Analysis","authors":[{"name":"Aimene Belfodil","slug":"aimene-belfodil","given":"Aimene","family":"Belfodil","affiliation":"Univ Lyon , INSA Lyon , CNRS , LIRIS UMR 5205 , F-69621 , Lyon , France; Mobile Devices Ingenierie , 100 Avenue Stalingrad , 94800 , Villejuif , France"},{"name":"Sergei O. Kuznetsov","slug":"sergei-o-kuznetsov","given":"Sergei O.","family":"Kuznetsov","affiliation":"National Research University Higher School of Economics , Moscow , Russia"},{"name":"Céline Robardet","slug":"celine-robardet","given":"Céline","family":"Robardet","affiliation":"Univ Lyon , INSA Lyon , CNRS , LIRIS UMR 5205 , F-69621 , Lyon , France"},{"name":"Mehdi Kaytoue","slug":"mehdi-kaytoue","given":"Mehdi","family":"Kaytoue","affiliation":"Univ Lyon , INSA Lyon , CNRS , LIRIS UMR 5205 , F-69621 , Lyon , France"}],"abstract":"Pattern mining is an important task in AI for eliciting hypotheses from the data. When it comes to spatial data, the geo-coordinates are often considered independently as two different attributes. Consequently, rectangular patterns are searched for.  Such an arbitrary form is not able to capture interesting regions in general. We thus introduce convex polygons, a good trade-off for capturing high density areas in any pattern mining task. Our contribution is threefold: (i) We formally introduce such patterns in Formal Concept Analysis (FCA), (ii) we give all the basic bricks for mining polygons with exhaustive search and pattern sampling, and (iii) we design several algorithms that we compare experimentally.","doi":"10.24963/ijcai.2017/197","url":"https://ijcai.org/proceedings/2017/197","pdf":"https://ijcai.org/proceedings/2017/0197.pdf","markdown":"https://ijcai.org/proceedings/2017/197.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/197","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":197,"pages":{"first":1425,"last":1432},"keywords":["Machine Learning: Data Mining","Machine Learning: Unsupervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal and Han, 2014] Charu C. 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