Open Access
ARTICLE
An Apriori-Based Learning Scheme towards Intelligent Mining of Association Rules for Geological Big Data
Maojian Chen1,2,3, Xiong Luo1,2,3,*, Yueqin Zhu4, Yan Li1,2,3, Wenbing Zhao5, Jinsong Wu6
1 School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
2 Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 100083, China
3 Beijing Intelligent Logistics System Collaborative Innovation Center, Beijing, 101149, China
4 Development and Research Center, China Geological Survey, Beijing, 100037, China
5 Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, Ohio, 44115, USA
6 Department of Electrical Engineering, Universidad de Chile, Santiago, 1058, Chile
* Corresponding Author: Xiong Luo. Email:
Intelligent Automation & Soft Computing 2020, 26(5), 973-987. https://doi.org/10.32604/iasc.2020.010129
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.
Keywords
Cite This Article
M. Chen, X. Luo, Y. Zhu, Y. Li, W. Zhao
et al., "An apriori-based learning scheme towards intelligent mining of association rules for geological big data,"
Intelligent Automation & Soft Computing, vol. 26, no.5, pp. 973–987, 2020.
Citations