Analysis and Data Mining of Lead-Zinc Ore Data

Zanev, Vladimir, Topalov, Stanislav, and Christov, Veselin. "Analysis and Data Mining of Lead-Zinc Ore Data." Serdica Journal of Computing 7.3 (2013): 271-280. <http://eudml.org/doc/268661>.

@article{Zanev2013,
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.},
author = {Zanev, Vladimir, Topalov, Stanislav, Christov, Veselin},
journal = {Serdica Journal of Computing},
keywords = {Data Analysis; Data Mining; Clustering; Prediction},
language = {eng},
number = {3},
pages = {271-280},
publisher = {Institute of Mathematics and Informatics Bulgarian Academy of Sciences},
title = {Analysis and Data Mining of Lead-Zinc Ore Data},
url = {http://eudml.org/doc/268661},
volume = {7},
year = {2013},
}

TY - JOUR
AU - Zanev, Vladimir
AU - Topalov, Stanislav
AU - Christov, Veselin
TI - Analysis and Data Mining of Lead-Zinc Ore Data
JO - Serdica Journal of Computing
PY - 2013
PB - Institute of Mathematics and Informatics Bulgarian Academy of Sciences
VL - 7
IS - 3
SP - 271
EP - 280
AB - 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.
LA - eng
KW - Data Analysis; Data Mining; Clustering; Prediction
UR - http://eudml.org/doc/268661
ER -