Generation of agriculture data has increased over past years to judge impact of agriculture parameters to make action plan and to examine agriculture productivity. This data is generally spatio - temporal in nature and may have additional dimensions such as agriculture parameters (agriculture land, arable land etc), environmental attributes (Co2 emission etc) and geographical attributes (region, state etc). It's a challenging task to analyze this growing data and generate useful results. Various methods are available to analyze data which makes use of various parameters to generate results. In this paper first we build a multidimensional model of data then apply multidimensional analysis, statistical analysis (as co-relation) on multidimensional model and data mining technique (as association rule mining) on correlated data. Our analysis approach helps us to build model and apply advance techniques like multidimensional data analysis, statistical mining and data mining to extract knowledge from this model. There are various data collecting agencies like World Bank, IMF, Department of Economics and Statistics and lot of private agencies like ORG, AC-Nielsen. We have presented our approach using a case study to analyze agriculture productivity using various agriculture related parameters. We have used data available on World Bank website.
Hira, S., & Deshpande, P. S. (2015). Data Analysis using Multidimensional Modeling, Statistical Analysis and Data Mining on Agriculture Parameters. In Procedia Computer Science (Vol. 54, pp. 431–439). Elsevier. https://doi.org/10.1016/j.procs.2015.06.050