Application of semi-supervised fuzzy c-means method in clustering multivariate geochemical data, a case study from the Dalli Cu-Au porphyry deposit in central Iran

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• 作者：Moslem Fatehi^a ; Hooshang H. Asadi^a ; ^b
• 关键词：multivariate geochemical analysis ; semi-supervised ; ssFCM ; Dalli Cu-Au porphyry deposit
• 刊名：Ore Geology Reviews
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：81
• 期：part_P1
• 页码：245-255
• 全文大小：2558 K
• 卷排序：81

文摘

Semi-supervised fuzzy c-means method (ssFCM) is used to cluster multivariate soil geochemical data. Favorable classes of copper mineralization are identified by ssFCM for further drilling of Dalli Cu-Au porphyry deposit. Favorable classes are compared with the residual rock-channel and drill-core samples for validation. The ssFCM method reveals a better improvement in data clustering compared to the FCM method.