Multiple data clustering algorithms applied in search of patterns of clay minerals in soils close to an abandoned manganese oxide mine

This paper proposes a multi-level approach to data clustering and provides a novel approach to characterisation of clay soils by, effectively, looking at the same clay sample from different angles. It is shown that using this approach can help avoid detection of spurious clusters or skipping vital natural grouping in data. Muscovite, illite and kaolinite were identified by X-ray diffraction (XRD) in <4 μm fraction of soil samples obtained from the periphery of an abandoned manganese oxide mine and semi quantified as major, minor and trace. Based on information inherent in the data attributes, useful rules for grouping the samples were generated and with the aid of multiple data clustering, applied to characterize the clay minerals occurrences in the soils. The paper found that the presence of large quantities of illite and kaolinite heavily influence the formation of clusters. When the most influential variables—LJ and KJ were taken out, the resulting model showed that muscovite traces play a vital role in initial cluster building and the importance matrix of inputs suggested inter-dependence between muscovite, kaolinite and illite traces as well as between them and minor quantities of illite. Dwelling on aspects of clay mineralogy and modelling sciences, the paper marks a significant departure from the conventional approaches to clay characterisation by showing how effectively data mining methods can be adopted in the area. For a successful approach to characterisation of clay minerals in African soils, the paper recommends to set-up data repositories that will provide scientific data sources and forums in a multi-disciplinary environment. This is particularly important as capturing interesting patterns requires expert knowledge describing the emerging natural groupings.