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Data Mining Techniques in Agricultural and Environmental Sciences

Data Mining Techniques in Agricultural and Environmental Sciences

Altannar Chinchuluun, Petros Xanthopoulos, Vera Tomaino, P.M. Pardalos
Copyright: © 2010 |Volume: 1 |Issue: 1 |Pages: 15
ISSN: 1947-3192|EISSN: 1947-3206|ISSN: 1947-3192|EISBN13: 9781616929626|EISSN: 1947-3206|DOI: 10.4018/jaeis.2010101302
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MLA

Chinchuluun, Altannar, et al. "Data Mining Techniques in Agricultural and Environmental Sciences." IJAEIS vol.1, no.1 2010: pp.26-40. http://doi.org/10.4018/jaeis.2010101302

APA

Chinchuluun, A., Xanthopoulos, P., Tomaino, V., & Pardalos, P. (2010). Data Mining Techniques in Agricultural and Environmental Sciences. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 1(1), 26-40. http://doi.org/10.4018/jaeis.2010101302

Chicago

Chinchuluun, Altannar, et al. "Data Mining Techniques in Agricultural and Environmental Sciences," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 1, no.1: 26-40. http://doi.org/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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