Abstract
Quality control of climate data obtained from weather stations is essential to ensure reliability of research and services based on this data. One way to perform this control is to compare data received from one station with data from other stations which somehow are expected to show similar behavior. The purpose of this work is to evaluate some visual data mining techniques to identify groupings (and outliers of these groupings) of weather stations using historical precipitation data in a specific time interval. We present and discuss the techniques’ details, variants, results and applicability on this type of problem.
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Garcia, J.R.M., Monteiro, A.M.V., Santos, R.D.C. (2012). Visual Data Mining for Identification of Patterns and Outliers in Weather Stations’ Data. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_30
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DOI: https://doi.org/10.1007/978-3-642-32639-4_30
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