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A New Compound Function-Based Z-number Valued Clustering

  • B. G. Guirimov
  • O. H. Huseynov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)

Abstract

A large variety of hard and soft clustering methods exist including the deterministic, probabilistic and fuzzy clustering. However, these methods are devoted to handling different types of uncertainty. No works exist on clustering taking into account a confluence of probabilistic and fuzzy information. In such cases, reliability of extracted knowledge is one of the important issues to be studied. The concept of Z-number was introduced by Prof. Zadeh as a formal construct that express reliability of information under bimodal distribution. In this paper we suggest an approach to Z-number valued clustering of large data sets to describe reliability of data-driven knowledge. A numerical example is given that confirms validity of the proposed method.

Keywords

Data mining Z-number Clustering Reliability 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.SOCAR Midstream OperationsBakuAzerbaijan
  2. 2.Research Laboratory of Intelligent Control and Decision Making Systems in Industry and EconomicsAzerbaijan State Oil and Industry UniversityBakuAzerbaijan

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