Multivariate geostatistical data have become omnipresent in the geosciences and pose
substantial analysis challenges. One of them is the grouping of data locations into spatially …

The subdivision of samples into stationary sets is one of the first decisions in a resource
modeling workflow where geologically and statistically related samples are grouped for …

With the increasing development of remote sensing platforms and the evolution of sampling
facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform …

Domaining is very often a complex and time-consuming process in mining assessment.
Apart from the delineation of envelopes, a significant number of parameters (lithology …

The detection of spatially contiguous clusters is a relevant task in geostatistics since near
located observations might have similar features than distant ones. Spatially compact …

A growing interest in clustering spatial data is emerging in several areas, from local
economic development to epidemiology, from remote sensing data to environment analyses …

Clustering constitutes one of the most popular and important tasks in data analysis. This is
true for any type of data, and geographic data is no exception. In fact, in geographic …

We explore and compare different methods for the spatial clustering of geostatistical data. A
new methodology based on the likelihood is proposed and compared to the approach by …

The particularities of geosystems and geoscience data must be understood before any
development or implementation of statistical learning algorithms. Without such knowledge …

There are many statistical techniques that allow finding similarities or differences among
data and variables. Cluster analysis encompasses many diverse techniques for discovering …