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@InProceedings{PeñaAndeRochArag:2015:NoAlCl,
               author = "Peña, Lorena Gayarre and Anderson, Liana Oighenstein and Rocha, 
                         Guilherme Concei{\c{c}}{\~a}o and Arag{\~a}o, Luiz Eduardo 
                         Oliveira e Cruz de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and {} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Novo algoritmo de classifica{\c{c}}{\~a}o autom{\'a}tica de 
                         dados multidimensionais para identifica{\c{c}}{\~a}o de 
                         comportamentos, limiares de decis{\~a}o e outliers com potencial 
                         utiliza{\c{c}}{\~a}o para dados de sensores remotos",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "5248--5255",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "A scientific research starts with a data processing. This process 
                         can be divided in three steps which, depending on their 
                         characteristics can be applied in a sequential fashion: 1) 
                         Outliers research (data that can be considered erroneous), 2) 
                         Behaviour identification, and 3) Behaviour threshold definition. 
                         Most of the times, the success of a research depends on an 
                         adequate accomplishment of the three steps mentioned above, 
                         defining accurate thresholds which provide confiability and 
                         decreases errors. Many times, this work is carried out by using 
                         the manual trial and error methodology until the optimal 
                         thresholds are found. Usually, these thresholds must be adjusted, 
                         and this process may be repeated many times in case one wants to 
                         apply the results to another dataset. This study proposes a new 
                         multi-dimensional data automatic classification algorithm which 
                         can be used in an exploratory analysis. This algorithm provides a 
                         data characterization, pointing out outliers, determining 
                         decisions thresholds and identifying data behaviours using less 
                         time than the required to do it via the trial and error 
                         methodology. In this research, the algorithm is applied to three 
                         remote sensing study cases, demonstrating both time and human 
                         resources economy and validating the results.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "1035",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4DPQ",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3JM4DPQ",
           targetfile = "p1035.pdf",
                 type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
        urlaccessdate = "25 jan. 2021"
}


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