Submitted by Secretaria Pós de Produção (tpp@vm.uff.br) on 2017-07-27T19:24:17Z No. of bitstreams: 1 D2016 - Glauco Barbosa da Silva.pdf: 5776264 bytes, checksum: d50812d465487f52e8c02b7f636435ab (MD5) / Made available in DSpace on 2017-07-27T19:24:17Z (GMT). No. of bitstreams: 1 D2016 - Glauco Barbosa da Silva.pdf: 5776264 bytes, checksum: d50812d465487f52e8c02b7f636435ab (MD5) / Problem: From Modeling decision maker preferences, the Multicriteria Decision Aid (MCDA) is a field dedicated to the study of real-world decision-making problems that are, usually, too complex and not so well-structured to be considered through the examination of a single point of view (criteria). This feature of MCDA implies that a comprehensive model of a decision maker situation cannot be “created”, but instead, the model should be developed to meet the requirements of the Decision Maker (DM). In general, the development of such a model can only be achieved through an iterative and interactive process, until the preferences of the decision maker are consistently represented in the model. However, an interactive method is a procedure that consists of an alternation of calculation and discussion stages, which presumes that the decision maker is willing to answer a large number of relatively difficult questions. For instance, one of the main difficulties to be faced when interacting with a Decision Maker in order to build a decision aid procedure is the various parameters’ elicitation of the preference model. Methodology: In this thesis, as an alternative to interactive process, among the main streams of MCDA, a Preference Disaggregation Analysis method was used, which is considered to assess or to infer preference models from the given preferential structures and to address decision-aiding activities to elicit preferential information and to construct decision models from decision examples. Combining Composition Probabilistic of Preference with Data Mining techniques, a proposal of a three-step process is presented: attribute selection; clustering; and classification. The first and the second ones are data mining tasks and the last one is a multicriteria task. Purpose: This thesis aims to present a new approach with a Data Mining Layer (attribute selection and/or clustering) in Composition Probabilistic of Preferences in Trichotomic procedure (CPP-TRI), which combines data mining techniques with a Multicriteria Decision Aid method in classification (sorting) problems. Findings: The decision maker ability to comprehend without powerful tools has been exceeded. Therefore, important decisions are often made based not on the information-rich data stored in data repositories, but rather, on intuition of the decision maker. Involved in similar problems, the connections between disaggregation methods and data mining (identifying patterns, extracting knowledge from data, eliciting preferential information and constructing decision models from decision examples) are explored to combine and improve the CPP-TRI Method from Attribute Selection Techniques. / Problem: From Modeling decision maker preferences, the Multicriteria Decision Aid (MCDA) is a field dedicated to the study of real-world decision-making problems that are, usually, too complex and not so well-structured to be considered through the examination of a single point of view (criteria). This feature of MCDA implies that a comprehensive model of a decision maker situation cannot be “created”, but instead, the model should be developed to meet the requirements of the Decision Maker (DM). In general, the development of such a model can only be achieved through an iterative and interactive process, until the preferences of the decision maker are consistently represented in the model. However, an interactive method is a procedure that consists of an alternation of calculation and discussion stages, which presumes that the decision maker is willing to answer a large number of relatively difficult questions. For instance, one of the main difficulties to be faced when interacting with a Decision Maker in order to build a decision aid procedure is the various parameters’ elicitation of the preference model. Methodology: In this thesis, as an alternative to interactive process, among the main streams of MCDA, a Preference Disaggregation Analysis method was used, which is considered to assess or to infer preference models from the given preferential structures and to address decision-aiding activities to elicit preferential information and to construct decision models from decision examples. Combining Composition Probabilistic of Preference with Data Mining techniques, a proposal of a three-step process is presented: attribute selection; clustering; and classification. The first and the second ones are data mining tasks and the last one is a multicriteria task. Purpose: This thesis aims to present a new approach with a Data Mining Layer (attribute selection and/or clustering) in Composition Probabilistic of Preferences in Trichotomic procedure (CPP-TRI), which combines data mining techniques with a Multicriteria Decision Aid method in classification (sorting) problems. Findings: The decision maker ability to comprehend without powerful tools has been exceeded. Therefore, important decisions are often made based not on the information-rich data stored in data repositories, but rather, on intuition of the decision maker. Involved in similar problems, the connections between disaggregation methods and data mining (identifying patterns, extracting knowledge from data, eliciting preferential information and constructing decision models from decision examples) are explored to combine and improve the CPP-TRI Method from Attribute Selection Techniques.