1887
Volume 42, Issue 4
  • ISSN: 0812-3985
  • E-ISSN: 1834-7533

Abstract

[

Fuzzy Gustafson–Kessel cluster analysis is employed to integrate suites of disparate data sets. The fuzzy -means algorithm is used as a reference to discuss the advantages of the Gustafson–Kessel algorithm and revise a database comprising airborne and ground-based geophysical data sets while minimising preparatory data processing required for fuzzy -means cluster analysis.

, Abstract

The fuzzy partitioning Gustafson-Kessel cluster algorithm is employed for rapid and objective integration of multi-parameter Earth-science related databases. We begin by evaluating the Gustafson-Kessel algorithm using the example of a synthetic study and compare the results to those obtained from the more widely employed fuzzy -means algorithm. Since the Gustafson-Kessel algorithm goes beyond the potential of the fuzzy -means algorithm by adapting the shape of the clusters to be detected and enabling a manual control of the cluster volume, we believe the results obtained from Gustafson-Kessel algorithm to be superior. Accordingly, a field database comprising airborne and ground-based geophysical data sets is analysed, which has previously been classified by means of the fuzzy -means algorithm. This database is integrated using the Gustafson-Kessel algorithm thus minimising the amount of empirical data processing required before and after fuzzy -means clustering. The resultant zonal geophysical map is more evenly clustered matching regional geology information available from the survey area. Even additional information about linear structures, e.g. as typically caused by the presence of dolerite dykes or faults, is visible in the zonal map obtained from Gustafson-Kessel cluster analysis.

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2011-12-01
2026-01-21
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  • Article Type: Research Article
Keyword(s): airborne; cluster analysis; data integration; fuzzy c-means; Gustafson-Kessel; South Africa

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