Geodesic
Analysis and Data Mining of Lead-Zinc Ore Data
Serdica Journal of Computing, Tome 7 (2013) no. 3, pp. 271-280.

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This paper presents the results of our data mining study of Pb-Zn (lead-zinc) ore assay records from a mine enterprise in Bulgaria. We examined the dataset, cleaned outliers, visualized the data, and created dataset statistics. A Pb-Zn cluster data mining model was created for segmentation and prediction of Pb-Zn ore assay data. The Pb-Zn cluster data model consists of five clusters and DMX queries. We analyzed the Pb-Zn cluster content, size, structure, and characteristics. The set of the DMX queries allows for browsing and managing the clusters, as well as predicting ore assay records. A testing and validation of the Pb-Zn cluster data mining model was developed in order to show its reasonable accuracy before beingused in a production environment. The Pb-Zn cluster data mining model can be used for changes of the mine grinding and floatation processing parameters in almost real-time, which is important for the efficiency of the Pb-Zn ore beneficiation process. ACM Computing Classification System (1998): H.2.8, H.3.3.
Keywords: Data Analysis, Data Mining, Clustering, Prediction 
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     title = {Analysis and {Data} {Mining} of {Lead-Zinc} {Ore} {Data}},
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Zanev, Vladimir; Topalov, Stanislav; Christov, Veselin. Analysis and Data Mining of Lead-Zinc Ore Data. Serdica Journal of Computing, Tome 7 (2013) no. 3, pp. 271-280. http://geodesic.mathdoc.fr/item/SJC_2013_7_3_a5/