Mining Convex Polygon Patterns with Formal Concept Analysis

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 shapes 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 between expressiv-ity and algorithmic complexity. Our contribution is threefold: (i) We formally introduce such patterns in Formal Concept Analysis (FCA), (ii) we give all the basic bricks for mining convex polygons with exhaustive search and pattern sampling, and (iii) we design several algorithms, which we compare experimentally.
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Communication dans un congrès
Carles Sierra. The Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI), Aug 2017, Melbourne, Australia. https://www.ijcai.org/proceedings/2017/, pp.1425 - 1432, 2017, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence Main track. 〈https://www.ijcai.org/proceedings/2017/〉. 〈10.24963/ijcai.2017/197〉
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Soumis le : jeudi 10 août 2017 - 17:01:17
Dernière modification le : jeudi 19 avril 2018 - 14:38:06

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Aimene Belfodil, Sergei Kuznetsov, Céline Robardet, Mehdi Kaytoue. Mining Convex Polygon Patterns with Formal Concept Analysis. Carles Sierra. The Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI), Aug 2017, Melbourne, Australia. https://www.ijcai.org/proceedings/2017/, pp.1425 - 1432, 2017, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence Main track. 〈https://www.ijcai.org/proceedings/2017/〉. 〈10.24963/ijcai.2017/197〉. 〈hal-01573841〉

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