Investigating Factors that Influence Rice Yields of Bangladesh using Data Warehousing, Machine Learning, and Visualization
Fahad Shabbir Ahmed, Dip Nandi, Mashiour Rahman, Khandaker Tabin Hasan · International Journal of Modern Education and Computer Science · 2017
In this paper, we have tried to identify the prominent factors of Rice production of all the three seasons of the year (Aus, Aman, and Boro) by applying K-Means clustering on climate and soil variables' data warehoused using Fact Constellation schema.For the clustering, the popular machine-learning tool Weka was used whose visualization feature was principally useful to determine the patterns, dependencies, and relationships of rice yield on different climate and soil factors of rice production.