Varieties of approaches and algorithms have been presented to identify
the distribution of elements. Previous researches based on the type of problem,
categorized their data in proper clusters or classes. This means that the
process of solution could be supervised or unsupervised. In cases, where there
is no idea about dependency of samples to specific groups, clustering methods
(unsupervised) are applied. About geochemistry data, since various elements are
involved, in addition to the complex nature of geochemical data, clustering
algorithms would be useful for recognition of elements distribution. In this
paper, Self-Organizing Map (SOM) algorithm, as an unsupervised method, is
applied for clustering samples based on REEs contents. For this reason the
Choghart Fe-REE deposit (Bafq district, central Iran), was selected as study
area and dataset was a collection of 112 lithology samples that were assayed
with laboratory tests such as ICP-MS and XRF analysis. In this study, input
vectors include 19 features which are coordinates x, y, z and concentrations of
REEs as well as the concentration of Phosphate (P2O5) since the apatite is the main source of REEs in
this particular research. Four clusters were determined as an optimal number of
clusters using silhouette criterion as well as k-means clustering method and
SOM. Therefore, using self-organizing map, study area was subdivided in four
zones. These four zones can be described as phosphate type, albitofyre type,
metasomatic and phosphorus iron ore, and Iron Ore type. Phosphate type is the
most prone to rare earth elements. Eventually, results were validated with
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