FCM Algorithm: A Partition-Based Clustering Algorithm with Fuzzy Logic Implementation

The Fuzzy C-Means (FCM) algorithm is a partition-based clustering method designed to maximize similarity within clusters while minimizing inter-cluster similarity. As an improvement over hard-partitioning C-means algorithms, FCM employs flexible fuzzy partitioning using membership functions. This description covers fuzzy set fundamentals crucial for implementing FCM, including membership degree calculations and iterative optimization procedures in clustering applications.