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Data Mining Techniques in Agricultural and Environmental Sciences
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Author(s): Altannar Chinchuluun (University of Florida, USA), Petros Xanthopoulos (University of Florida, USA), Vera Tomaino (University of Florida, USA and University Magna Græcia of Catanzaro, Italy)and P.M. Pardalos (University of Florida, USA)
Copyright: 2010
Volume: 1
Issue: 1
Pages: 15
Source title:
International Journal of Agricultural and Environmental Information Systems (IJAEIS)
Editor(s)-in-Chief: Frederic Andres (National Institute of Informatics, Japan), Chutiporn Anutariya (Asian Institute of Technology, Thailand), Teeradaj Racharak (Japan Advanced Institute of Science and Technology, Japan)and Watanee Jearanaiwongkul (National institute of Informatics, Japan)
DOI: 10.4018/jaeis.2010101302
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Abstract
Data mining techniques are largely used in different sectors of the economy and they increasingly are playing an important role in agriculture and environment-related areas. This paper aims to show our vision on the importance of knowing and efficiently using data mining and machine learning-related techniques for knowledge discovery in the field of agriculture and environment. Efforts for searching hidden patterns in data are not a recent phenomenon. History shows that extensive observations on data have helped discover empirical laws in different fields of research. Therefore, it is important to provide researchers in agriculture and environmental-related areas with the most advanced knowledge discovery techniques. Data mining is the process of extracting important and useful information from large sets of data. This information can be converted into useful knowledge that could help to better understand the problem in study and to better predict future developments. The paper presents the state of the art in data mining and knowledge discovery techniques and provides discussions for future directions.
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