On the Use of Ontology as a priori Knowledge into Constrained Clustering

Abstract : Recent studies have shown that the use of a priori knowledge can significantly improve the results of unsupervised classification. However, capturing and formatting such knowledge as constraints is not only very expensive requiring the sustained involvement of an expert but it is also very difficult because some valuable information can be lost when it cannot be encoded as constraints. In this paper, we propose a new constraint-based clustering approach based on ontology reasoning for automatically generating constraints and bridging the semantic gap in satellite image labeling. The use of ontology as a priori knowledge has many advantages that we leverage in the context of satellite image interpretation. The experiments we conduct have shown that our proposed approach can deal with incomplete knowledge while completely exploiting the available one.
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Communication dans un congrès
IEEE International Conference on Data Science and Advanced Analytics (DSAA), Oct 2016, Montreal, Canada. IEEE DSAA 2016 3rd IEEE International Conference on Data Science and Advanced Analytics 〈https://sites.ualberta.ca/~dsaa16/〉
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https://hal.archives-ouvertes.fr/hal-01400122
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Dernière modification le : mardi 19 mars 2019 - 01:24:01
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  • HAL Id : hal-01400122, version 1

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Hatim Chahdi, Nistor Grozavu, Isabelle Mougenot, Laure Berti-Equille, Younès Bennani. On the Use of Ontology as a priori Knowledge into Constrained Clustering. IEEE International Conference on Data Science and Advanced Analytics (DSAA), Oct 2016, Montreal, Canada. IEEE DSAA 2016 3rd IEEE International Conference on Data Science and Advanced Analytics 〈https://sites.ualberta.ca/~dsaa16/〉. 〈hal-01400122〉

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