Abstract
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 a crucial player in the Kenyan economy, and it vastly contributes to the provision of job opportunities for millions of Kenyan citizens as well as strengthening the Gross Domestic Product (GDP). Therefore, efforts toward strengthening this sector are highly warranted. Mining the past agricultural data to establish any new knowledge is, hence of great essence. Knowledge discovery is a crucial component of modern-day decision-making. In the agricultural sector, the knowledge gained from past data can be used for various beneficial purposes, including planning, budgeting, and forecasting 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.
