Optimization Of Fuzzy Logic-Based Clustering For Wireless Sensor Networks

A strong framework for managing complicated and uncertain data patterns is provided by fuzzy C-Means (FCM) clustering, a potent and frequently used data analysis technique that is adaptable for data clustering in a variety of applications, including pattern recognition, picture segmentation, and data mining. Assigning data points to various clusters with different degrees of membership, in contrast to typical hard clustering approaches, makes FCM especially well-suited for situations where data show ambiguity and overlap. An overview of FCM is given in this abstract, emphasizing its fundamental ideas, iterative optimization procedure, and capacity to manage intricate data patterns. It also goes over the benefits and uses of FCM in practical situations, highlighting how it can be used to provide cluster assignments that are both flexible and nuanced. For researchers and practitioners looking for efficient ways to cluster data even in the face of ambiguity and incomplete memberships, FCM remains a useful tool.