Abstract
This paper introduces a hybrid clustering/classification method for successfully solving the popular clustering/ classifying complex datasets problems. The proposed method for the solution of the clustering /classifying problem, designated as PFV-index method, combines a particle swarm optimization (PSO) algorithm, Fuzzy C-Means (FCM) method, Variable Precision Rough Sets (VPRS) theory and a new cluster validity index function. This method could cluster the values of the individual attributes within the dataset and achieves both the optimal number of clusters and the optimal classification accuracy. The validity of the proposed approach is investigated by comparing the classification results obtained for UCI datasets with those obtained by supervised classification BPNN, decision-tree methods.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
