D-CoA: Probability Density-based Confidence Assignment Semi-Supervised Clustering Ensemble

In recent years, semi-supervised clustering ensembles have gained significant attention in the field of clustering. These ensembles leverage limited prior knowledge to guide the clustering process. Nonetheless, throughout the integration process, owing to the employment of diverse fundamental clustering algorithms, disparate algorithms generate incongruent results. Consequently, ascertain a more reliable conclusion becomes a formidable challenge. In this paper, we introduce D-CoA (Density based Confidence of Assignment), a novel semi-supervised clustering ensemble algorithm, designed to navigate the intricacies of this issue. D-CoA incorporates sub-clusters using the Minimum Spanning Tree algorithm and evaluates the confidence of each amalgamated cluster to establish prior confidence. Subsequently, a Mixture Density Network is leveraged to calculate the probability density distribution for each merged cluster, yielding posterior confidence scores for resolving conflicts in sample assignments. By adopting this methodology, we amplify the reliability of conflicting sample assignments. Empirical results demonstrate the versatility of this approach, as it effectively accommodates probability distributions of varying shapes, thus demonstrating its feasibility and scalability for diverse data types.