Predicting unknown mineral localities based on mineral associations.

Mindat (mindat.org), the Mineral Evolution Database (RRUFF.info/Evolution), and the Global Earth Mineral Inventory are some of the well-known datasets in the field of mineralogy, which contain data about almost all known localities on Earth where minerals have been found. The increase in the amount and accuracy of mineral data and the improvements in technological resources make it possible to explore and answer large, outstanding scientific questions, such as, understanding the mineral assemblages on Earth and how they compare to assemblages and localities on other planets.. In this contribution, we present an affinity analysis method to: 1) Predict the previously unknown occurrence of a mineral of interest. 2) Predict the location of a geologic environment or deposit based on a mineral assemblage. Affinity Analysis, or Market Basket Analysis, is a machine learning method that uses mined association rules to find interesting patterns in the data. The strength of the rules is identified using some measures of interestingness, such as ‘lift’. For example, when the occurrence of a mineral predicted with high confidence at a given locality is unexpected (low support), the rule used for such a prediction is considered ‘very interesting’. Successful implementation of this methodology will greatly aid the mineral discovery process.