Incremental fuzzy cluster ensemble learning based on rough set theory

To deal with the uncertainty, vagueness and overlapping distribution within the data sets, a novel incremental fuzzy cluster ensemble method based on rough set theory (IFCERS) is proposed by the idea of combining clustering analysis task with classification techniques. Firstly, on the basis of soft clustering results, the positive region, boundary region and negative region of clustering ensemble are obtained by applying the construction of rough approximation in rough set theory, and then a group structure within data points of positive region is obtained by adopting a fuzzy cluster ensemble method. Secondly, by combining with the supervised ensemble learning method, e.g., random forests, the obtained group structure is used to construct the random forests classifier to classify the data points in boundary region. Finally, all the acquired group structure is used to train the random forests classifier to classify the data points of negative region. Experimental evaluations on UCI machine learning repository datasets verify the effectiveness of the proposed method. It is also shown that the quality of the final solution has a weak correlation with the ensemble size, the parameter setting on the rough approximations construction is appropriate, and the proposed method is robust towards the diversity from hard clustering members.