Density based spatial clustering of applications with noise and fuzzy C-means algorithms for unsupervised mineral prospectivity mapping

Our research focuses on examining two clustering methods, namely Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and fuzzy c-means (FCM) algorithms, to create prospectivity models for Cu mineralization in the Kerman belt, SE Iran. Traditional clustering techniques struggle with...

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Bibliographic Details
Published in:	Earth science informatics Vol. 18; no. 2; p. 217
Main Authors:	Ghezelbash, Reza, Daviran, Mehrdad, Maghsoudi, Abbas, Hajihosseinlou, Mahsa
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2025 Springer Nature B.V
Subjects:	Algorithms Bioinformatics Cluster analysis Clustering Copper Density Earth and Environmental Science Earth Sciences Earth System Sciences Factor analysis Fuzzy control Geochemistry Geographic information systems Geological mapping Image processing Information Systems Applications (incl.Internet) Interpolation Mineral deposits Mineralization Network analysis Ontology Parameter identification Remote sensing Signatures Simulation and Modeling Social networks Space Exploration and Astronautics Space Sciences (including Extraterrestrial Physics Cu-porphyry deposits DBSCAN Unsupervised MPM Kerman Fuzzy c-means
ISSN:	1865-0473, 1865-0481
Online Access:	Get full text
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