Detecção de outliers espaciais: refinamento de similaridade e desempenho

• Autor(a) principal: Kawabata, Thatiane [UNESP]
• Data de Publicação: 2015
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://hdl.handle.net/11449/127787; http://www.athena.biblioteca.unesp.br/exlibris/bd/cathedra/14-09-2015/000846509.pdf
• topic: Computação; Sistemas de informação geografica; Sistemas de dados especiais; Mineração de dados (Computação); Algoritmos de computador

The progress and development of technologies used to collect spatial information resulted in an increase in the amount of spatial data stored in databases. This also caused many problems, common in large databases, such as data redundancy, incomplete data, unknown values and outliers. Aiming to obtain relevant information from spatial data, the application of algorithms for exploration of spatial data, especially spatial clusters of algorithms, has become a fairly common practice across the world scene. Moreover, many current algorithms ignore the presence of local outliers in spatial data, or just consider your location in relation to other data in base, which can cause inconsistent results and complicate the extraction of knowledge. Thus, in order to contribute to this, the work aims to develop a survey of information related to exploration of spatial data, detection of conventional and spatial outliers, as well as, present the main work in state of the art. Finally, we propose to provide a portable and configurable algorithms to the results of spatial clustering approach, which includes an improvement on an algorithm to detect spatial outliers, aimed at prospecting for information in the dataset