Abstract
This work proposes a methodology that reduces the error of future estimations in commercialization based on multivariate spatial prediction techniques (cokriging) considering the products with strong associations. It is based on the Apriori algorithm to find association rules in sales of agricultural products of local markets. Results show the improvement in spatial prediction accuracy after using the best association rules.
Highlights
Farming is an economic and social sector provider of food in the world that guarantees processes of food security within a country
We present other works that use data mining techniques used in this research oriented to different domains establishing validity and probity of the algorithms used such as association rules, kriging, and cokriging, in addition to the relationship with topics oriented in the same line of research
Association Rules and Apriori Algorithm. e rst way to establish a relationship between products in this research is based on the number of times some products appear together in a sale transaction [13], and for this it is necessary to discretize the transactional data le, so that if a product is acquired, it is identi ed with a value Tof true and F if it is not a part. is di erentiation of products acquired allows to establish the minimum support that is known as the relationship between the number of times a product appears in a transaction with respect to the total of transactions made, and this process is repeated for a single item
Summary
Farming is an economic and social sector provider of food in the world that guarantees processes of food security within a country. E third work [12] focuses on the estimation of the commercialization of products using their geographical location and their relationship as an influence in the improvement of consumption predictions based on the set of items resulting from the application of association rules.
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