A Niching Memetic Algorithm for Simultaneous Clustering and Feature Selection-IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 20, NO. 7,
Abstract—
Clustering is inherently a difficult task and is made even more difficult when the selection of relevant features is also an
issue. In this paper, we propose an approach for simultaneous clustering and feature selection using a niching memetic algorithm. Our
approach (which we call NMA_CFS) makes feature selection an integral part of the global clustering search procedure and attempts to
overcome the problem of identifying less promising locally optimal solutions in both clustering and feature selection, without making
any a priori assumption about the number of clusters. Within the NMA_CFS procedure, a variable composite representation is devised
to encode both feature selection and cluster centers with different numbers of clusters. Furthermore, local search operations are
introduced to refine feature selection and cluster centers encoded in the chromosomes. Finally, a niching method is integrated to
preserve the population diversity and prevent premature convergence. In an experimental evaluation, we demonstrate the effectiveness of the proposed approach by using both synthetic and real data.