Z-Number Clustering Based on General Type-2 Fuzzy Sets

  • Rafik AlievEmail author
  • Babek Guirimov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)


Clustering is a convenient tool to extract or summarize information from large data sets. Data sets collected by modern information systems are constantly increasing in size. These data sets may include imprecise and partially reliable information. Usually this uncertainty is both probabilistic and fuzzy. Unfortunately, up-to-date there is almost no research on clustering which takes into account a synergy of both probability and fuzziness in produced information. In this paper, we first suggest an approach to clustering large data sets with probabilistic and fuzzy uncertainties. This approach is based on relationship of general Type-2 Fuzzy and Z-number concepts. A numerical example is considered to demonstrate the validity of the proposed method.


Z-number C-means clustering Z-clustering Type-2 fuzzy set 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Azerbaijan State Oil and Industry UniversityBakuAzerbaijan
  2. 2.SOCAR Midstream OperationsBakuAzerbaijan

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