An Innovative Semi-Supervised Fuzzy Clustering Technique Using Cluster Boundaries

Active semi-supervised fuzzy clustering integrates fuzzy clustering techniques with limited labeled data, guided by active learning, to enhance classification accuracy, particularly in complex and ambiguous datasets. Although several active semi-supervised fuzzy clustering methods have been developed previously, they typically face significant limitations, including high computational complexity, sensitivity to initial cluster centroids, and difficulties in accurately managing boundary clusters where data points often overlap among multiple clusters. This study introduces a novel Active Semi-Supervised Fuzzy Clustering algorithm specifically designed to identify, analyze, and correct misclassified boundary elements. By strategically utilizing labeled data through active learning, our method improves the robustness and precision of cluster boundary assignments. Extensive experimental evaluations conducted on three types of datasets—including benchmark UCI datasets, synthetic data with controlled boundary overlap, and satellite imagery—demonstrate that our proposed approach achieves superior performance in terms of clustering accuracy and robustness compared to existing active semi-supervised fuzzy clustering methods. The results confirm the effectiveness and practicality of our method in handling real-world scenarios where precise cluster boundaries are critical.