Research on Association Rules Reasoning and Application of Geosciences Data Based on Ameliorated Trapezoidal Cloud Transformation

Abstract : This paper proposes an association rules reasoning model based on ameliorated trapezoidal cloud transformation. It is aimed primarily at complexity and randomness geosciences data bears. The traditional trapezoidal cloud transformation is improved in order to avoid lack of data mutation information and to finish reasonable and sensitive exchange from qualification to quantification. A set of attributes for simulating faults extraction algorithm is designed, which breaks through limitations of traditional visual interpretation and ensures an effectiveness and completeness of test data. Multi-Level Association Rules (MLAR) model [1] is also adopted to reason and predict unknown faults and fault properties in Chengdu Office zone. The result shows that the MLAR algorithm enhanced an association mining between fault types with their classified attributes.
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Communication dans un congrès
Maria Cecilia Calani Baranauskas; Kecheng Liu; Lily Sun; Vânia Paula de Almeida Neris; Rodrigo Bonacin; Keiichi Nakata. 17th International Conference on Informatics and Semiotics in Organisations (ICISO), Aug 2016, Campinas, Brazil. Springer International Publishing, IFIP Advances in Information and Communication Technology, AICT-477, pp.127-132, 2016, Socially Aware Organisations and Technologies. Impact and Challenges. 〈10.1007/978-3-319-42102-5_14〉
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Xu Jing. Research on Association Rules Reasoning and Application of Geosciences Data Based on Ameliorated Trapezoidal Cloud Transformation. Maria Cecilia Calani Baranauskas; Kecheng Liu; Lily Sun; Vânia Paula de Almeida Neris; Rodrigo Bonacin; Keiichi Nakata. 17th International Conference on Informatics and Semiotics in Organisations (ICISO), Aug 2016, Campinas, Brazil. Springer International Publishing, IFIP Advances in Information and Communication Technology, AICT-477, pp.127-132, 2016, Socially Aware Organisations and Technologies. Impact and Challenges. 〈10.1007/978-3-319-42102-5_14〉. 〈hal-01646551〉

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