Fuzzy Systems and its Applications

Fuzzy Systems and its Applications

Clustering based on fuzzy population; When and how?

Document Type : Original Article

Authors
Marzieh Rezaei 1
Abbas Parchami 2
Ayyub Sheikhy 1
1 Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
2 Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman,
10.22034/jfsa.2025.523784.1273
Abstract
Various methods have been proposed for clustering data extracted from a precise population. However, in practice, we may encounter situations where the population is imprecise or fuzzy, such as a fuzzy population of high-consumption households, fuzzy population of high-quality products from a factory, fuzzy population of elderly individuals, or fuzzy population of high-income households. In such cases, each data point extracted from the population includes not only the observed value but also the degree of its membership in the fuzzy population. This paper introduces a novel method for clustering data extracted from a fuzzy population, which is an extension of the k-means algorithm. Additionally, a practical example is presented to clarify the concept of fuzzy population and demonstrate the performance of the proposed algorithm.
Keywords
k-means algorithm
membership function
data extracted from a fuzzy population
weighting
Subjects
 

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Fuzzy Systems and its Applications
Volume 8, Issue 2 - Serial Number 17
Open Access Statement
December 2025
Pages 91-108

  • Receive Date 15 May 2025
  • Revise Date 02 August 2025
  • Accept Date 01 October 2025