Investigating Factors that Influence Rice Yields of Bangladesh using Data Warehousing, Machine Learning, and Visualization

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Fahad Ahmed 1,* Dip Nandi 1 Mashiour Rahman 1 Khandaker Tabin Hasan 1

1. American International University-Bangladesh, Dhaka-1213,Bangladesh

* Corresponding author.


Received: 6 Nov. 2016 / Revised: 9 Dec. 2016 / Accepted: 23 Jan. 2017 / Published: 8 Mar. 2017

Index Terms

Fact Constellation, K-Means Clustering, Visualization, Elbow Method


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.

Cite This Paper

Fahad Ahmed, Dip Nandi, Mashiour Rahman, Khandaker Tabin Hasan, "Investigating Factors that Influence Rice Yields of Bangladesh using Data Warehousing, Machine Learning, and Visualization", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.3, pp.36-47, 2017. DOI:10.5815/ijmecs.2017.03.05


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