Mining Epidemiological Dengue Fever Data from Brazil: A Gradual Pattern Based Geographical Information System

Abstract : Dengue fever is the world’s fastest growing vector-borne disease. Studying such data aims at better understanding the behaviour of this disease to prevent the dengue propagation. For instance, it may be the case that the number of cases of dengue fever in cities depends on many factors, such as climate conditions, density, sanitary conditions. Experts are interested in using geographical information systems in order to visualize knowledge on maps. For this purpose, we propose to build maps based on gradual patterns. Such maps provide a solution for visualizing for instance the cities that follow or not gradual patterns.
Type de document :
Communication dans un congrès
IPMU: Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jul 2014, Montpellier, France. 15th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part II, Communications in Computer and Information Science (443), pp.414-423, 2014, Information Processing and Management of Uncertainty in Knowledge-Based Systems. 〈10.1007/978-3-319-08855-6_42〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01381088
Contributeur : Anne Laurent <>
Soumis le : vendredi 14 octobre 2016 - 00:33:20
Dernière modification le : mardi 20 février 2018 - 14:56:01

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Yogi Satrya Aryadinata, Yuan Lin, Christovam Barcellos, Anne Laurent, Thérèse Libourel Rouge. Mining Epidemiological Dengue Fever Data from Brazil: A Gradual Pattern Based Geographical Information System. IPMU: Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jul 2014, Montpellier, France. 15th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part II, Communications in Computer and Information Science (443), pp.414-423, 2014, Information Processing and Management of Uncertainty in Knowledge-Based Systems. 〈10.1007/978-3-319-08855-6_42〉. 〈lirmm-01381088〉

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