The Association Rules Algorithm Based on Clustering in Mining Research in Corn Yield

  • Bo Liu
  • Guifen ChenEmail author
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 509)


With the popularization of agricultural information technology, the use of data mining techniques to analyze the impact of different types of soil nutrient content and yield of corn has become a hot topic in the field of agriculture. Association rule mining is an important part of the field in Data mining, association rules can be found associated with agricultural data attributes. This article will use cluster analysis and association rule to analysis correlation between corn yield and soil nutrient. Firstly compare different clustering algorithm to chooses the optimal algorithm, make data collected in scientific classification, and based on expert knowledge of the collected data into different levels; then determine the type and content of different soil by association rules corn yield and soil nutrient; final inspection algorithm is correct. The results showed that: comparing K-means, hierarchical clustering analysis, and PAM, K-means algorithm to determine the optimal clustering; K value can be determined at selected intervals. K is equal to 3, 4 or 6, clustering effect is good according to Sil value when K from 3 to 10. Based on the principle of association rules, clustering algorithm to select a K value associated with the combination of rule 6; After clustering algorithm of association rules, support and credibility and improve degree of accuracy is better than not clustering; by mining association rules after clustering, a great influence on the different levels of soil nutrients in corn yield. The results for the corn yield provides intelligent decision support data.


Corn yield Cluster analysis Association rules Frequent itemsets Apriori algorithm 



Funds for this research was provided by National “863” project “corn precision operating systems research and demonstration” (2006AA10A309), National spark plan “corn” digital technology integration and demonstration (2008GA661003), Jilin provincial departments of world bank loan project (2011-Z20), Jilin province rural project in 2015 (Study and demonstration of corn precision operation system based on Internet of things).


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© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  1. 1.JiLin Agricultural University College of Information TechnologyChangchunChina

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