What question did this study set out to answer?

The aim is to enhance traditional clustering techniques by incorporating specific requirements for cluster center distributions.

Improved Fuzzy C‐Means Clustering With Cluster Center Constraints

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The improved clustering algorithm enhances accuracy in constrained environments, suggesting practical applications.

The aim is to enhance traditional clustering techniques by incorporating specific requirements for cluster center distributions.
Developed an enhanced Fuzzy C-means Clustering algorithm with cluster center constraints.
Introduced a dynamic adjustment mechanism using penalty functions.
Conducted various experiments to evaluate algorithm efficacy in constrained scenarios.
Achieved a Constraint Satisfaction Degree of 0.97735.
Obtained a Uniformity Index of 0.65141.
Recorded a Region Coverage Rate of 0.53056, indicating superior performance over traditional clustering methods.

Mingxuan et al. (2026) studied this question.

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