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An Innovative Decision-Making Approach Based on Correlation Coefficients of Complex Picture Fuzzy Sets and Their

Jianping Qu1, Abdul Nasir2, Sami Ullah Khan2

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This study introduces a new decision-making (DM) algorithm using complex picture fuzzy sets (CPFS) to handle uncertainty. The algorithm is validated through a product classification clustering problem, demonstrating its effectiveness in real-world applications.

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Area of Science:

  • Decision Sciences
  • Fuzzy Mathematics
  • Computer Science

Background:

  • Decision-making (DM) is crucial for organizational success.
  • Uncertainty and ambiguity often complicate DM processes.
  • Fuzzy set theory offers tools to manage imprecise information.

Purpose of the Study:

  • To design an innovative DM algorithm utilizing complex picture fuzzy sets (CPFS).
  • To introduce novel concepts like CPFS information energy and correlation measures.
  • To apply and validate the algorithm in a practical business scenario.

Main Methods:

  • Development of a novel DM algorithm for various fuzzy information types.
  • Definition of new CPFS-based concepts: information energy, correlation, and matrix composition.
  • Application of the algorithm to a product classification clustering problem.

Main Results:

  • The proposed algorithm effectively handles diverse fuzzy information.
  • New CPFS-related mathematical concepts and properties are established.
  • Experimental validation through product clustering demonstrates the algorithm's utility.

Conclusions:

  • The developed CPFS-based DM algorithm provides a robust framework for uncertain environments.
  • The novel mathematical tools enhance the capability of fuzzy set theory in DM.
  • The algorithm's successful application in product classification validates its practical significance.