Decisions fusion strategy: Towards hybrid cluster ensemble

Clustering ensembles have renowned as a powerful methodfor improving both the performance and constancy ofunsupervised classification solutions. However, finding aconsensus clustering from multiple algorithms is a difficultproblem that can be approached from combinatorial orstatistical perspectives. We offer a new clustering strategywhich is formulated to cluster extracted mammographyfeatures into soft clusters using unsupervised learningstrategies and ‘fuse’ the decisions using majority voting andparallel fusion in conjunction with a neural classifier. Theidea is to observe associations in the features and fuse thedecisions (made by learning algorithms) to find the strongclusters which can make impact on overall classificationaccuracy. Two novel techniques are proposed for fusion,majority-voting based data fusion, and neural-based fusion.The proposed approaches are tested and evaluated on thebenchmark database— digital database for screeningmammograms (DDSM). This study compares the performanceof the proposed ensemble approach with other fusionapproaches for clustering ensembles. Experimental resultsdemonstrate the effectiveness of the proposed method onbenchmark dataset.<p></p>