A niching memetic algorithm for simultaneous clustering and feature selection

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. Further, 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 and compare it with other related approaches, using both synthetic and real data

k-Component q-deformed charge coherent states and their nonclassical properties

k-Component q-deformed charge coherent states are constructed, their (over)completeness proved and their generation explored. The q-deformed charge coherent states and the even (odd) q-deformed charge coherent states are the two special cases of them as k becomes 1 and 2, respectively. A D-algebra realization of the SU$_q$(1,1) generators is given in terms of them. Their nonclassical properties are studied and it is shown that for $k\geq3$, they exhibit two-mode q-antibunching, but neither SU$_q$(1,1) squeezing, nor one- or two-mode q-squeezing.Comment: LaTeX, 29 pages, 2 Postscript figures, minor change