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Application of a data mining framework for the identification of agricultural production areas in WA
Conference paper   Open access

Application of a data mining framework for the identification of agricultural production areas in WA

Y. Vagh, L.J. Armstrong and D. Diepeveen
14th Pacific-Asia Conference on Knowledge Discovery and Data Mining (Hyderabad, India, 21/06/2010–24/06/2010)
2010
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Abstract

This paper will propose a data mining framework for the identification of agricultural production areas in W.A. The data mining (DM) framework was developed with the aim of enhancing the analysis of agricultural datasets compared to currently used statistical methods. The DM framework is a synthesis of different technologies brought together for the purpose of enhancing the interrogation of these datasets. The DM framework is based on the data, information, knowledge and wisdom continuum as a horizontal axis, with DM and online analytical processing (OLAP) forming the vertical axis. In addition the DM framework incorporates aspects of data warehousing phases, exploratory data mining (EDM and a post-processing phase for cyclic updating of data and for data qualification. The DM framework could be used to identify agricultural production areas in WA specifically for crop prediction, planting and harvesting strategies. In addition, farmers using the results from the DM framework may be able to better devise tactical and strategic plans brought about by seasonal variability and climatic changes. These outcomes all form part of a recommendation for best practices in agricultural production. Such a framework could also be used in a general context to analyze datasets in keeping with the attribute of reusability that all frameworks must display.

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