Fuzzy clustering with spatial correction and its application to geometallurgical domaining
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(Accepted version)
Date
2018
Authors
Sepulveda Escobedo, E.M.
Dowd, P.A.
Xu, C.
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Journal Title
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Journal article
Citation
Mathematical Geosciences, 2018; 50(8):895-928
Statement of Responsibility
E. SepĂșlveda, P. A. Dowd, C. Xu
Conference Name
Abstract
This paper describes a proposed method for clustering attributes on
the basis of their spatial variability and the uncertainty of cluster member-
ship. The method is applied to geometallurgical domaining in mining ap-
plications. The main objective of geometallurgical clustering is to ensure
consistent feed to a processing plant by minimising transitions between
di erent types of feed coming from di erent domains (clusters). For this
purpose, clusters should contain not only similar geometallurgical char-
acteristics but also be located in as few contiguous and compact spatial
locations as possible so as to maximise the homogeneity of ore delivered
to the plant. Most existing clustering methods applied to geometallurgy
have two problems. Firstly, they are unable to di erentiate subsets of
attributes at the cluster level and therefore cluster membership can only
be assigned on the basis of exactly identical attributes, which may not be
the case in practice. Secondly, as they do not take account of the spatial
relationships they can produce clusters which may be spatially dispersed
and/or overlapped. In the work described in this paper a new clustering
method is introduced that integrates three distinct steps to ensure qual-
ity clustering. In the rst step, fuzzy membership information is used to
minimise compactness and maximise separation. In the second step, the
best subsets of attributes are de ned and applied for domaining purposes.
These two steps are iterated to convergence. In the nal step a graph-
based labelling method, which takes spatial constraints into account, is
used to produce the nal clusters. Three examples are presented to illus-
trate the application of the proposed method. These examples demon-
strate that the proposed method can reveal useful relationships among
geometallurgical attributes within a clear and compact spatial structure.
The resulting clusters can be used directly in mine planning to optimise
the ore feed to be delivered to the processing plant.
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Published online: 25 July 2018
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© International Association for Mathematical Geosciences 2018