Analysis and Data Mining of Lead-Zinc Ore Data
2014; Volume: 7; Issue: 3 Linguagem: Inglês
10.55630/sjc.2013.7.271-280
ISSN1314-7897
AutoresVladimir Zanev, Stanislav Topalov, Veselin Christov,
Tópico(s)Data Mining Algorithms and Applications
ResumoThis 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.
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