Principal component analysis and K-means clustering as tools during exploration for Zn skarn deposits and industrial carbonates, Sala area, Sweden

Jansson, Nils; Allen, Rodney L.; Skogsmo, Göran; Tavakoli, Saman

doi:10.1016/j.gexplo.2021.106909

Cited by 31 publications

(10 citation statements)

References 30 publications

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“…Fang et al [36] classified the Northeast China Cold Vortex activity paths into four types through KMC. Jansson et al [37] combined principal component analysis and KMC to classify the rocks of two deposits in the Sara area of Sweden.…”

Section: Cluster Analysismentioning

confidence: 99%

A pXRF-Based Approach to Identifying the Material Source of Stone Cultural Relics: A Case Study

et al. 2022

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Precise identification of material sources is of great significance to archaeological study, conservation, and restoration of stone cultural relics. The present study proposes a simple and efficient approach to identifying the material source of stone cultural relics based on portable X-ray fluorescence spectrometer (pXRF) and statistical analyses. Chemical elements of stone samples, including Ca, K, Fe, Al, Si, Cl, S, and Mg from both cultural relics and potential quarries, were first measured non-destructively using a pXRF device. Obtained chemical element data were then classified using statistical techniques (i.e., cluster analysis and principal component analysis) to match tested materials from cultural relics to the material from a quarry, thereby identifying the material source of stone cultural relics. The proposed method was applied to identify the material sources of the Jin Gang Throne Tower (JGT Tower), the stele of “Rebuilding Pu’ansi Temple” (PAS Stele), and the stele of “Renovation of Sanjinmiao Temple” (SJM Stele) in Beijing Stone Carving Art Museum. The study shows that pXRF can be used on-site for handheld, fast, inexpensive, and non-destructive measurements of the elemental composition of stone materials, being a powerful tool for identifying the material source of stone culture relics especially immovable and large-scale ones.

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Section: Cluster Analysismentioning

confidence: 99%

A pXRF-Based Approach to Identifying the Material Source of Stone Cultural Relics: A Case Study

et al. 2022

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“…Finally, Jansson et al [7] used the k-means cluster algorithm and the principle component analysis (PCA) to a group and reduce the huge data of whole-rock, multivariate lithogeochemical. They examined an unsupervised, data-driven methodology for subdividing calcareous marble samples based on evaluating (64) distinct geochemical parameters in whole-rock lithogeochemical data and bright spectrometric data on (181) pieces of dolomitic marble from Sala inlier.…”

Section: Related Workmentioning

confidence: 99%

K-means variations analysis for translation of English Tafseer Al-Quran text

Ahmed

Baharin

Nohuddin

2023

IJECE

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Text mining is a powerful modern technique used to obtain interesting information from huge datasets. Text clustering is used to distinguish between documents that have the same themes or topics. The absence of the datasets ground truth enforces the use of clustering (unsupervised learning) rather than others, such as classification (supervised learning). The “no free lunch” (NFL) theorem supposed that no algorithm outperformed the other in a variety of conditions (several datasets). This study aims to analyze the k-means cluster algorithm variations (three algorithms (k-means, mini-batch k-means, and k-medoids) at the clustering process stage. Six datasets were used/analyzed in chapter Al-Baqarah English translation (text) of 286 verses at the preprocessing stage. Moreover, feature selection used the term frequency–inverse document frequency (TF-IDF) to get the weighting term. At the final stage, five internal cluster validations metrics were implemented silhouette coefficient (SC), Calinski-Harabasz index (CHI), C-index (CI), Dunn’s indices (DI) and Davies Bouldin index (DBI) and regarding execution time (ET). The experiments proved that k-medoids outperformed the other two algorithms in terms of ET only. In contrast, no algorithm is superior to the other in terms of the clustering process for the six datasets, which confirms the NFL theorem assumption.

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“…To select fewer principal components to meet the analyzed practical problem requirement, the value of the cumulative variance explained by eigenvalues needs to be greater than 80%. In actual data processing, in order to eliminate the dimensional infuence of variables, the sample data need to be standardized before PCA [23]. At present, there are many standardization methods for data processing, but there is no general rule to select the best method.…”

Section: And Theexpression For the Frst Principal Component Is Ymentioning

confidence: 99%

Modeling GPDA and Its Application in Deep Mineral Prediction in the Jiguanzui Cu‐Au Mining Area in Hubei, China

Qin

Liu

Wang

et al. 2023

Mathematical Problems in Engineering

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Geochemical anomalies are the basis of mineral deposit prediction. Through the study of the characteristics of geochemical anomalies, we found that their distribution was consistent with a generalized Pareto distribution (GPD). In the present study, we designed a model for geochemical anomaly extraction via a GPD. In the designed GPD model, we used the kurtosis method to estimate the threshold value of the GPD. Furthermore, a principal component analysis (PCA) was used to extract comprehensive information of different geochemical elements in which minerals are enriched. On this basis, a new algorithm named the GPDA model was designed for deep mineral prediction by using the GPD and PCA, and the methods of the GPDA for selecting parameters were studied. The study data for Ba, Pb, As, Cu, Au, Mo, Co, and Zn originated from 26 exploration lines of the Jiguanzui Cu-Au mining area in Hubei, China. The proposed GPDA model was applied to deep mineral prediction in the study area. We estimated the parameters of the GPDA model, and the thresholds of Ba, Pb, As, Cu, Au, Mo, Co, and Zn were 457.8612, 56.1823, 28.8454, 910.1272, 89.4283, 34.5267, 84.9445, and 121.4863, respectively. The comprehensive information threshold value was 0.4551. The comprehensive abnormal distribution area of geochemical element contents was obtained from thresholds. The results showed that the method used to identify abnormal areas was consistent with the range of ore bodies identified by actual engineering exploration, demonstrating that the GPDA model was effective. Finally, we predicted that there was a new blind ore body located at a depth of about 1100 m below ground between drill holes KZK10 and KZK11. The results have important theoretical and practical significance for deep ore prospecting.

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Principal component analysis and K-means clustering as tools during exploration for Zn skarn deposits and industrial carbonates, Sala area, Sweden

Cited by 31 publications

References 30 publications

A pXRF-Based Approach to Identifying the Material Source of Stone Cultural Relics: A Case Study

A pXRF-Based Approach to Identifying the Material Source of Stone Cultural Relics: A Case Study

K-means variations analysis for translation of English Tafseer Al-Quran text

Modeling GPDA and Its Application in Deep Mineral Prediction in the Jiguanzui Cu‐Au Mining Area in Hubei, China

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