A novel hierarchical clustering analysis method based on Kullback–Leibler divergence and application on dalaimiao geochemical exploration data. (February 2019)

• Record Type: Journal Article
• Authors: Yang, Jie; Grunsky, Eric; Cheng, Qiuming
• Is Part Of: Computers & geosciences. Volume 123(2019)
• Page Start: 10
• Page End: 19
• Publication Date: 2019-02
• Subjects: Kullback–Leibler divergence -- Hierarchical cluster analysis -- Geochemical exploration data -- Geochemical pattern -- Data mining; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2018.11.003
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: In this paper, we propose a new hierarchical clustering analysis method (HCA) that uses Kullback–Leibler divergence ( D K L S ) of pairwise geochemical datasets of geo-objects (e.g., lithological units) as a measure of proximity. The method can reveal relationships among geo-objects based on geochemistry. This capability is verified through an application with geochemical exploration data from regolith that overlies the Dalaimiao region in China. D K L S M and D K L S C, two parts of D K L S, respectively describe the differences on the mean and the differences on covariance and are also used as measures of proximity. D K L S M characterizes rock type and D K L S C describes spatial relationships and component similarities between geo-objects. This contribution not only provides a tool that can reveal relationships between geo-objects based on geochemical data, but also reveals that D K L S and its two parts can characterize geochemical differences from different perspectives. These measures hold promise in the enhancement of methods for recognizing geochemical patterns. Highlights: A new hierarchical clustering analysis method based on KL divergence is proposed. The method can reveal relationships among geo-objects based on geochemistry. This capability is verified through an application with geochemical data.