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Geochemical Behavior Investigation Based on K-Means Clustering: Basics, Concepts and Case Study - Softcover
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One of the most important viewpoints of data mining concerning the analysis of a large amount of data and samples with different characteristics includes clustering which is associated with methods and techniques that have frequently been applied in different fields of researches. A well-known algorithm of clustering is K-means by which the data are divided into K classes based upon a distance criterion. In this study, the K-means method applies for data classification derived from exploration boreholes and surface sampling in the Parkam deposit. The results derived based on the case of borehole data showed that the increasing Cu grade values resulted in a significant increase in Mo grades, a significant decrease in Pb grades followed by an increase, and the Zn grades varying comparable to Pb. With regards to the relationships between these elements it could be concluded that the meteoric waters promoted the mobilization of Pb and Zn from the potassic to the phyllic zone but the meteoric waters were not effective enough to cause the mobilization of Cu, and this element together with Mo remained immobile. Finally, equation of Cu grade estimation is also presented for surface data.
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One of the most important viewpoints of data mining concerning the analysis of a large amount of data and samples with different characteristics includes clustering which is associated with methods and techniques that have frequently been applied in different fields of researches. A well-known algorithm of clustering is K-means by which the data are divided into K classes based upon a distance criterion. In this study, the K-means method applies for data classification derived from exploration boreholes and surface sampling in the Parkam deposit. The results derived based on the case of borehole data showed that the increasing Cu grade values resulted in a significant increase in Mo grades, a significant decrease in Pb grades followed by an increase, and the Zn grades varying comparable to Pb. With regards to the relationships between these elements it could be concluded that the meteoric waters promoted the mobilization of Pb and Zn from the potassic to the phyllic zone but the meteoric waters were not effective enough to cause the mobilization of Cu, and this element together with Mo remained immobile. Finally, equation of Cu grade estimation is also presented for surface data.
Biografía del autor
Dr. Ardeshir Hezarkhani. PhD, Geochemistry, Mineral Exploration and Modeling from McGill University, Canada. Post Doc, fellow at McGill University. Faculty member (Full professor) at Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
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Geochemical Behavior Investigation Based on K-Means Clustering
Verlag:
LAP LAMBERT Academic Publishing Dez 2015, 2015
ISBN 10: 3659811211
ISBN 13: 9783659811210
Neu
Taschenbuch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -One of the most important viewpoints of data mining concerning the analysis of a large amount of data and samples with different characteristics includes clustering which is associated with methods and techniques that have frequently been applied in different fields of researches. A well-known algorithm of clustering is K-means by which the data are divided into K classes based upon a distance criterion. In this study, the K-means method applies for data classification derived from exploration boreholes and surface sampling in the Parkam deposit. The results derived based on the case of borehole data showed that the increasing Cu grade values resulted in a significant increase in Mo grades, a significant decrease in Pb grades followed by an increase, and the Zn grades varying comparable to Pb. With regards to the relationships between these elements it could be concluded that the meteoric waters promoted the mobilization of Pb and Zn from the potassic to the phyllic zone but the meteoric waters were not effective enough to cause the mobilization of Cu, and this element together with Mo remained immobile. Finally, equation of Cu grade estimation is also presented for surface data. 60 pp. Englisch. Bestandsnummer des Verkäufers 9783659811210
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Geochemical Behavior Investigation Based on K-Means Clustering
Verlag:
LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659811211
ISBN 13: 9783659811210
Neu
Softcover
Anbieter: moluna, Greven, Deutschland
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Hezarkhani ArdeshirDr. Ardeshir Hezarkhani. PhD, Geochemistry, Mineral Exploration and Modeling from McGill University, Canada. Post Doc, fellow at McGill University. Faculty member (Full professor) at Amirkabir University of Technol. Bestandsnummer des Verkäufers 159145776
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Geochemical Behavior Investigation Based on K-Means Clustering
Verlag:
LAP LAMBERT Academic Publishing Dez 2015, 2015
ISBN 10: 3659811211
ISBN 13: 9783659811210
Neu
Taschenbuch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. Neuware -One of the most important viewpoints of data mining concerning the analysis of a large amount of data and samples with different characteristics includes clustering which is associated with methods and techniques that have frequently been applied in different fields of researches. A well-known algorithm of clustering is K-means by which the data are divided into K classes based upon a distance criterion. In this study, the K-means method applies for data classification derived from exploration boreholes and surface sampling in the Parkam deposit. The results derived based on the case of borehole data showed that the increasing Cu grade values resulted in a significant increase in Mo grades, a significant decrease in Pb grades followed by an increase, and the Zn grades varying comparable to Pb. With regards to the relationships between these elements it could be concluded that the meteoric waters promoted the mobilization of Pb and Zn from the potassic to the phyllic zone but the meteoric waters were not effective enough to cause the mobilization of Cu, and this element together with Mo remained immobile. Finally, equation of Cu grade estimation is also presented for surface data.Books on Demand GmbH, Überseering 33, 22297 Hamburg 60 pp. Englisch. Bestandsnummer des Verkäufers 9783659811210
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Geochemical Behavior Investigation Based on K-Means Clustering : Basics, Concepts and Case Study
Verlag:
LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659811211
ISBN 13: 9783659811210
Neu
Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - One of the most important viewpoints of data mining concerning the analysis of a large amount of data and samples with different characteristics includes clustering which is associated with methods and techniques that have frequently been applied in different fields of researches. A well-known algorithm of clustering is K-means by which the data are divided into K classes based upon a distance criterion. In this study, the K-means method applies for data classification derived from exploration boreholes and surface sampling in the Parkam deposit. The results derived based on the case of borehole data showed that the increasing Cu grade values resulted in a significant increase in Mo grades, a significant decrease in Pb grades followed by an increase, and the Zn grades varying comparable to Pb. With regards to the relationships between these elements it could be concluded that the meteoric waters promoted the mobilization of Pb and Zn from the potassic to the phyllic zone but the meteoric waters were not effective enough to cause the mobilization of Cu, and this element together with Mo remained immobile. Finally, equation of Cu grade estimation is also presented for surface data. Bestandsnummer des Verkäufers 9783659811210
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