@Article{iasc.2020.010129,
AUTHOR = {Maojian Chen, Xiong Luo, Yueqin Zhu, Yan Li, Wenbing Zhao, Jinsong Wu},
TITLE = {An Apriori-Based Learning Scheme towards Intelligent Mining of Association  Rules for Geological Big Data},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {5},
PAGES = {973--987},
URL = {http://www.techscience.com/iasc/v26n5/40818},
ISSN = {2326-005X},
ABSTRACT = {The past decade has witnessed the rapid advancements of geological 
data analysis techniques, which facilitates the development of modern agricultural 
systems. However, there remains some technical challenges that should be 
addressed to fully exploit the potential of those geological big data, while 
gathering massive amounts of data in this application field. Generally, a good 
representation of correlation in the geological big data is critical to making full use 
of multi-source geological data, while discovering the relationship in data and 
mining mineral prediction information. Then, in this article, a scheme is proposed 
towards intelligent mining of association rules for geological big data. Firstly, we 
achieve word embedding via word2vec technique in geological data. Secondly, 
through the use of self-organizing map (SOM) and K-means algorithm, the word 
embedding data is clustered to serve the purpose of improving the performance of 
analysis and mining. On the basis of it, the unsupervised Apriori learning 
algorithm is developed to analyze and mine these association rules in data. Finally, 
some experiments are conducted to verify that our scheme can effectively mine 
the potential relationships and rules in the mineral deposit data.},
DOI = {10.32604/iasc.2020.010129}
}