fuzzy cmeans clustering algorithm

An ensemble clustering has been considered as one of the research approaches in data mining, pattern recognition, machine learning and artificial intelligence over the last decade. In clustering, the combination first produces several bases clustering, and then, for their aggregation, a function is used to create a final cluster that is as similar as possible to all the cluster bundles. The input of this function is all base clusters and its output is a clustering called clustering agreement. This function is called an agreement function. Ensemble clustering has been proposed to increase efficiency, strong, reliability and clustering stability. Because of the lack of cluster monitoring, and the inadequacy of general-purpose base clustering algorithms on the other, a new approach called an ensemble clustering has been proposed in which it has been attempted to find an agreed cluster with the highest Consensus and agreement. In fact, ensemble clustering techniques with this slogan, the combination of several poorer models, is better than a strong model. However, this claim is correct if certain conditions (such as the diversity between the members in the consensus and their quality) are met. This article presents an ensemble clustering method. This paper uses the weak clustering method of fuzzy cmeans as a base cluster. Also, by adopting some measures, the diversity of consensus has increased. The proposed hybrid clustering method has the benefits of the clustering algorithm of fuzzy cmeans that has its speed, as well as the major weaknesses of the inability to detect non-spherical and non-uniform clusters. In the experimental results, we have tested the proposed ensemble clustering algorithm with different, up-to-date and robust clustering algorithms on the different data sets. Experimental results indicate the superiority of the proposed ensemble clustering method compared to other clustering algorithms to up-to-date and strong.