Clustering is inherently a difficult task and is made even more difficult when the selection of relevant features is also an issue. In this project, we introduce 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.
Here it is just a abstract you can download the full project documentation in:
final1(8.4) - it Contains first contents,bonafide,and other details....The full documentation is in the Following file: Full Report
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