Cluster Size-Constrained Fuzzy C-Means with Density Center Searching

Fuzzy C-means (FCM) has a definite limitation when partitioning a dataset into clusters with varying sizes and densities because it ignores the scale difference in different dimensions of input data objects. To alleviate this cluster size insensitivity, we propose a wrapper algorithm for FCM by introducing cluster size as a priori information and limiting the search direction on the basis of density benchmarks (CSCD-FCM). This method is divided into two stages. The first stage adjusts the position of each cluster while maintaining its shape, and the second stage changes the shape of each cluster while maintaining its center. Both steps modify fuzzy partitions generated by FCM-like soft clustering methods by optimizing a "size-constrained" objective function. Numerical and practical experiments with unbalanced cluster size settings demonstrate the effectiveness of this method for extracting actual cluster structures, as well as achieving the desired cluster populations.