Visual Data Mining for Identification of Patterns and Outliers in Weather Stations’ Data

• Published in: Intelligent Data Engineering and Automated Learning - IDEAL 2012 Vol. 7435; pp. 245 - 252
• Main Authors: Garcia, José Roberto M., Monteiro, Antônio Miguel V., Santos, Rafael D. C.
• Format: Book Chapter
• Language: English
• Published: Germany Springer Berlin / Heidelberg 2012; Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: clustering; fuzzy C-means; self-organizing map; Visual data mining
• ISBN: 3642326382; 9783642326387
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-32639-4_30

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.