Abstract
This paper offers a comprehensive exploration of partition validation functions, specifically focusing on partition coefficient and partition entropy within the realm of fuzzy clustering—an influential approach in the field of clustering datasets. While fuzzy clustering facilitates the classification of data points into multiple clusters, the pivotal tasks of determining the optimal number of clusters and evaluating the validity of the resultant clusters pose inherent challenges. The study addresses these challenges, contributing to the broader understanding of effective fuzzy clustering methodologies.
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