Integrating time series mining and fractals to discover patterns and extreme events in climate and remote sensing databases.

• Ano de defesa: 2010
• Autor(a) principal: ROMANI, L. A. S.
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Palavras-chave em Português: Mineração de textos; Séries temporais; Séries climáticas; Text mining; Agrometeorologia; Sensoriamento remoto; Time series analysis; Remote sensing; Agrometeorology; Databases
• Link de acesso: http://www.alice.cnptia.embrapa.br/alice/handle/doc/876360

This thesis presents new methods based on fractal theory and data mining techniques to support agricultural monitoring in regional scale, specifically regions with sugar cane fields. This commodity greatly contributes to the Brazilian economy since it is a viable alternative to replace fossil fuels. Since climate influences the national agricultural production, researchers use climate data associated to agrometeorological indexes, and recently they also employed data from satellites to support decision making processes. In this context, we proposed a method that uses the fractal dimension to identify trend changes in climate series jointly with a statistical analysis module to define which attributes are responsible for the behavior alteration in the series. Moreover, we also proposed two methods of similarity measure to allow comparisons among different agricultural regions represented by multiples variables from meteorological data and remote sensing images. Given the importance of studying the extreme weather events, which could increase in intensity, duration and frequency according to different scenarios indicated by climate forecasting models, we proposed the CLIPSMiner algorithm to identify relevant patterns and extremes in climate series. CLIPSMiner also detects correlations among multiple time series considering time lag and finds patterns according to parameters, which can be calibrated by the users. We applied two distinct approaches in order to discover association patterns on time series. The first one is the Apriori-FD method that integrates an algorithm to perform attribute selection through applying the correlation fractal dimension, an algorithm of discretization to convert continuous values of series into discrete intervals, and a well-known association rules algorithm (Apriori). Although Apriori-FD has identified interesting patterns related to temperature, this method failed to appropriately deal with time lag. As a solution, we proposed CLEARMiner that is an unsupervised algorithm in order to mine the association patterns in one time series relating them to patterns in other series considering the possibility of time lag. The proposed methods were compared with similar techniques as well as assessed by a group of meteorologists, and specialists in agrometeorology and remote sensing. The experiments showed that applying data mining techniques and fractal theory can contribute to improve the analyses of agrometeorological and satellite data. These new techniques can aid researchers in their work on decision making and become important tools to support decision making in agribusiness.