Open Access

ARTICLE

An Apriori-Based Learning Scheme towards Intelligent Mining of Association Rules for Geological Big Data

Maojian Chen^1,2,3, Xiong Luo^1,2,3,*, Yueqin Zhu⁴, Yan Li^1,2,3, 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.

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Citations