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The application of data mining techniques to characterize agricultural soil profiles
Armstrong, L., Diepeveen, D. and Maddern, R.
The advances in computing and information storage have
provided vast amounts of data. The challenge has been to
extract knowledge from this raw data; this has lead to new
methods and techniques such as data mining that can
bridge the knowledge gap. This research aimed to assess
these new data mining techniques and apply them to a soil
science database to establish if meaningful relationships
can be found.
A large data set extracted from the WA Department of
Agriculture and Food (AGRIC) soils database has been
used to conduct this research. The database contains
measurements of soil profile data from various locations
throughout the south west agricultural region of Western
Australia. The research establishes whether meaningful
relationships can be found in the soil profile data at
different locations. In addition, comparison was made
between current data mining techniques such as cluster
analysis and statistical methods to establish the most
effective technique. The outcome of the research may
have many benefits, to agriculture, soil management and
environmental |
Cite as: Armstrong, L., Diepeveen, D. and Maddern, R. (2007). The application of data mining techniques to characterize agricultural soil profiles. In Proc. Sixth Australasian Data Mining Conference (AusDM 2007), Gold Coast, Australia. CRPIT, 70. Christen, P., Kennedy, P. J., Li, J., Kolyshkina, I. and Williams, G. J., Eds. ACS. 85-100. |
(from crpit.com)
(local if available)
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