Based on Static And Dynamic:Fuzzy Logic-Based Clustering Optimization for Wireless Sensor Networks

Fuzzy C-Means (FCM) clustering, a widely employed and sophisticated data analysis method, offers a robust structure for handling intricate and uncertain data patterns. Its versatility extends to numerous domains, such as data mining, pattern recognition, and image segmentation. Unlike conventional hard clustering approaches, FCM is particularly suitable for scenarios involving ambiguous and overlapping data by assigning data points to distinct clusters with varying degrees of membership. This synopsis provides a summary of FCM, with particular emphasis on its core concepts, iterative optimization process, and capability to handle complex data patterns. It also describes the practical benefits and applications of FCM, emphasizing how it can be utilized to generate flexible and nuanced cluster assignments. Despite the presence of ambiguity and insufficient memberships, FCM continues to be a valuable tool for practitioners and researchers seeking efficient methods to cluster data.