wised semi-supervised cluster ensemble selection: a new framework for selecting and combing multiple partitions based on prior knowledge

the wisdom of crowds, an innovative theory described in social science, claims that the aggregate decisions made by a group will often be better than those of its individual members if the four fundamental criteria of this theory are satisfied. this theory used for in clustering problems. previous researches showed that this theory can significantly increase the stability and performance of learning problems. as a solution, this paper proposes a new methodology of using woc theory for evaluating and selecting basic result partitions in semi-supervised clustering problems. this paper introduces new technique for reducing the data dimensions based on supervision information, a new semi-supervised clustering algorithm based on k-means for generating basic results, a new strategy for evaluating and selecting basic results based on feedback mechanism, a new metric for evaluating diversity of basic results. the results demonstrate the efficiency of proposed method's aggregate decision-making compared to other algorithms.