NDLI: Mining metallogenic association rules combining cloud model with Apriori algorithm

Content Provider	IEEE Xplore Digital Library
Author	Ying Cui Binbin He Jianhua Chen Zhonghai He Yue Liu
Copyright Year	2010
Description	Author affiliation: College of Information Engineering, Chengdu University of Technology, 611731, China (Yue Liu) \|\| Institute of Geo-Spatial Information Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China (Ying Cui; Binbin He; Jianhua Chen; Zhonghai He)
Abstract	Spatial data mining refers to extracting and “mining” the hidden, implicit, valid, novel and interesting spatial or non-spatial patterns or rules from large-amount, incomplete, noisy, fuzzy, random, and practical spatial databases. Spatial association rules mining is extracted implicit association rules from spatial database. Many metallogenic association rules lie in geology spatial database. In this paper, a method for mineral resources prediction is proposed, which mainly including uncertainty transmission between qualitative and quantitative geology spatial data using cloud model, metallogenic association rules extracting using Apriori algorithm, and comprehensive assessment of rules. At last, an experiment of iron resources prediction is performed in Eastern Kunlun Mountains, China. The results indicated that the method proposed in this paper is suitable for regional metallogenic prediction.
Starting Page	4507
Ending Page	4510
File Size	386301
Page Count	4
File Format	PDF
ISBN	9781424495658
ISSN	21537003
e-ISBN	9781424495665
e-ISBN	9781424495641
DOI	10.1109/IGARSS.2010.5649947
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Association rules Iron Spatial databases Clouds Pragmatics Itemsets metallogenic prediction Spatial data mining Apriori algorithm cloud model
Content Type	Text
Resource Type	Article

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