Abstract

Prediction of Tea Production in Kenya Using Clustering and Association Rule Mining Techniques

As at present, the agricultural sector is the backbone of the Kenyan economy. Though there has been a significant focus on other emerging industries, the agriculture sector remains to be a crucial player in the Kenyan economy, and which vastly contributes in the provision of job opportunities for millions of Kenyan citizens as well as strengthening the Gross Domestic Product (GDP). Therefore, efforts towards strengthening this sector are highly and warranted. Mining the past agricultural data to establish any new knowledge is, hence of great essence. Knowledge discovery is a crucial component of the modern day decision making. In the agricultural sector, the knowledge gained from the past data can be used for various beneficial purposes, including planning, budgeting a forecasting the possible future production trends. This paper attempts to predict tea production in Kenya through step-wise use of the clustering and association rule data mining techniques. A conclusion is presented, based on the presented arguments.


Author(s): Nzuva Mutie Silas and Lawrence Nderu

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