CLEARMiner: A New Algorithm for Mining Association Patterns on Heterogeneous Time Series from Climate Data

摘要

Recently, improvements in sensor technology contributed to increasing in spatial data acquisition. The use of remote sensing in many countries and states, where agricultural business is a large part of their gross income, can provide a valuable source to improve their economy. The combination of climate and remote sensing data can reveal useful information, which can help researchers to monitor and estimate the production of agricultural crops. Data mining techniques are the main tools to analyze and extract relationships and patterns. In this context, this paper presents a new algorithm for mining association patterns in Geo-referenced databases of climate and satellite images. The CLEARMiner (CLi-matE Association patteRns Miner) algorithm identifies patterns in a time series and associates them with patterns in other series within a temporal sliding window. Experiments were performed with synthetic and real data of climate and NOAA-AVHRR sensor for sugar cane fields. Results show a correlation between agroclimate time series and vegetation index images. Rules generated by our new algorithm show the association patterns in different periods of time in each time series, pointing to a time-delay between the occurrences of patterns in the series analyzed, corroborating what specialists usually forecast having the burden of dealing with many data charts.