Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/2342

 Title: Analysis and Data Mining of Lead-Zinc Ore Data Authors: Zanev, VladimirTopalov, StanislavChristov, Veselin Keywords: Data AnalysisData MiningClusteringPrediction Issue Date: 2013 Publisher: Institute of Mathematics and Informatics Bulgarian Academy of Sciences Citation: Serdica Journal of Computing, Vol. 7, No 3, (2013), 271p-280p Abstract: 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. URI: http://hdl.handle.net/10525/2342 ISSN: 1312-6555 Appears in Collections: Volume 7 Number 3

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