## 5 Conclusions  
Our case study of association rule mining in the Chahanwusu River area yielded the following conclusions.  
(1) According to the global autocorrelation indicators, Au shows a random distribution in the study area, and 14 other elements have positive correlations, ranked from large to small: Sb, Zn, Pb, Cu, Cd, As, Sn, Bi, Ag, Mo, Co, W, La, and Nb. Compared with local [PERSON]'s C and local [PERSON]'s G, local [PERSON]'s I can identify points of HH, LL, LH, and HL clustering with a precise meaning for each category, which makes it a better local autocorrelation indicator for association rule mining.  
(2) Based on the univariate LMIC results, the proposed method successfully mined 15 association rules among various elements in the study area. Bivariate spatial cross correlation can also detect distribution-pattern details of the co-occurrence of pair elements compared with association rule mining. However, it cannot be used to efficiently explore massive geochemical datasets. In contrast, association rule mining can reveal the association among items in a large geochemical dataset.  
(3) Overlying the mining results of association rules on the faults, ore occurrences, and catchment areas, we found that more than 86% of the mined rule points are located within 1000 m of faults and near known ore occurrences, and the impact of stream transport on element co-occurrences is weak. Greater densities and numbers of points of association rules  
Figure 23: Rule \(\{\text{Cu}\ (\text{HH})\}\Rightarrow\{\text{Co}\ (\text{HH})\}\) overlayed with the main lithostrata.  
Figure 22: Rule \(\{\text{Zn}\ (\text{HH}),\ Ag\ (\text{HH})\}\Rightarrow\{\text{Cd}\ (\text{HH})\}\) overlayed with the main lithostrata.  
were found in the Proterozoic Baishahe Formation (Pt\({}_{1}\)b) and the Early Permian grandorioriite (P\({}_{1}\)\(\gamma\)g). Therefore, the association rules are closely related to specific geological features.  
The association rules mined in this paper are mainly high-value element co-occurrence. Where these combinations appear, higher concentrations of the element are more likely, which can improve the prediction of unknown ore deposits or occurrences. However, the mining efficiency of low-value element co-occurrence is low, and the local dilution of elements in the study area cannot be effectively detected. In the future, we will build an element-association rule database to find combinations of anomalies for known metallogenic elements and to map the probability of unknown mineralization in the study area.  
Conceptualization, B.Z. and J.D.; methodology, Y.C.; software, Z.J. and N.C.; validation, B.Z., Z.J. and Y.C.; data curation, Y.C.; writing--original draft preparation, B.Z. and Z.J.; writing--review and editing, B.Z. and U.K.; funding acquisition, B.Z. All authors have read and agreed to the published version of the manuscript.  
This study was supported by grants from the National Natural Science Foundation of China (Grant Nos. 42072326 and 41772348), China Geological Survey Project (Grant No. DD20190156), and the National Key Research and Development Program of China (Grant No. 2019 YFC1805905).  
Not applicable.  
The dataset of the current study is not publicly available due to a data privacy agreement we signed with The 8 th Team of Qinghai Provincial Bureau of Nonferrous Metals and Geological Exploration but are available from the corresponding author on reasonable request.  
The authors would like to thank the Co-Construction MapGIS Library by Engineering Research Center for Geographic Information System of China and Central South University for providing MapGIS\({}^{\circledR}\) software (Wuhan Zondy Cyber-Tech Co., Ltd., Wuhan, China). We also thank ZHANG [PERSON] (The 8 th Team of Qinghai Provincial Bureau of Nonferrous Metals and Geological Exploration) and [PERSON] (Central South University) for their kind assistance with data collection and [PERSON] (Central South University) for revising scientific English writing of this manuscript.  
The authors declare no conflict of interest.