Combining Graph Clustering and Quantitative Association Rules for Knowledge Discovery in Geochemical Data Problem

Abstract

Identifying geochemical patterns from backgrounds and generating associated mineralization remains challenging due to the complex structure of mineral deposits. To learn how to identify geochemical anomalies that are spatially associated with mineralization, we need in-depth knowledge of the dependence process. Quantitative association rules (QARs) are applied to discover remarkable relations and dependencies between attributes in a dataset, but it is difficult to generate relationships from geochemical data. In previous studies, no methodology to find association rules is proposed to deal with geochemical data problem, and the classical methods designed for Boolean and nominal attributes require previous discretization, which makes the whole process limited in processing complex data. In this paper, we proposed a hybrid method of graph clustering and quantitative association rules (GCQAR) as a new way of identifying significant geochemical patterns. Graph Clustering (GC) is used as partitioning paradigm because of its ability to handle large-scale datasets. The GC is based on modularity to effectively generate the groups of the graph, to avoid the over-partitioning, and to cover all the rules. In each partition, a set of geochemical quantitative association rules is produced. The results obtained in the experimental study performed on data collected in the field of Xiaoshan, Henan province, China. Our GCQAR has significant benefits in terms of recognition geochemical patterns compared to the traditional methods used in the field of geochemistry.

Keywords

• Recognition geochemical pattern
• quantitative association rules
• graph clustering
• modularity