Consultas por similaridade e mineração de regras de associação: maximizando o conhecimento extraído de séries temporais

• Autor(a) principal: Andrade, Claudinei Garcia de
• Data de Publicação: 2014
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFSCAR
• Texto Completo: https://repositorio.ufscar.br/handle/ufscar/583
• topic: Data mining (Mineração de dados); Análise de séries temporais; Regras de associação; Consultas por similaridade; Coulomb, Lei de; Data mining; Time series; Association rules; Similarity search; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

A time series analysis presents challenges. There is a difficulty to manipulate the data by requiring a large computational cost, or even, by the difficulty of finding subsequences that have the same characteristics. However, this analysis is important for understanding the evolution of various phenomena such as climate change, changes in financial markets among others. This project proposed the development of a method for performing similarity queries in time series that have better performance and accuracy than the state-of-art and a method of mining association rules in series using similarity. The experiments performed have applied the proposed methods in real data sets, bringing relevant knowledge, indicating that both methods are suitable for analysis by similarity of one-dimensional and multidimensional time series.