(Scfc)-C-2: Semi-Supervised Collaborative Fuzzy Clustering Method

This study presents a new knowledge-based fuzzy clustering called semi-supervised collaborative fuzzy clustering ((SCFC)-C-2), which emphasizes aggregating diverse knowledge sources rather than using them in separate steps to revealing structures of data objects stored across multiple data sites while maintaining data sharing restrictions. The proposed method aggregates fuzzy logic, semi-supervised and collaborative learning simultaneously into a unified objective function for both horizontal and vertical data site distributions. This unified behavior strengthens the principles of existing data analysis and the growth of the concept of knowledge-based fuzzy clustering. Also, the proposed objective function benefits from cases such as (a) using a more appropriate and compatible criterion with fuzzy concepts in reconciliation between the structure obtained from the data site and knowledge induced from semi-supervised and collaborative learning, (b) learning collaboration intensity between data sites (c) ability to adjust the fuzziness rate of the structures (d) explicit solutions for constituent variables. The proposed method is also reinforced with a preprocessing phase to resolve inconsistencies between the reference data site structure and received structures from other data sites before engaging in the collaboration phase with a fuzzy similarity measure based on set theoric measure. The comprehensive experimental results clearly indicate the importance of aggregation instead of knowledge sequential learning. Our method outperforms in both horizontal and vertical modes its series of rival techniques in terms of accuracy, precision, recall, specificity, NMI, ARI, and convergence speed.