Journal of Engineering Research

, 4:14

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

An automated parameter selection approach for simultaneous clustering and feature selection

  • Vijay KumarAffiliated withDepartment of Computer Science and Engineering, Thapar University Email author 
  • , Jitender K. ChhabraAffiliated withDepartment of Computer Engineering, National Institute of Technology
  • , Dinesh KumarAffiliated withDepartment of Computer Science and Engineering, Guru Jambheshwar University of Science & Technology


In this paper, an improved version of Niching Memetic Algorithm for Simultaneous Clustering and Feature Selection (NMA_CFS) is proposed. In NMA_CFS, the parameters such as replacement group size, selection group size and population size are determined empirically and are manually obtained after hit and trial experimentation. An automated approach is proposed to determine these parameters of NMA_CFS. The experimental results reveal that this modified NMA_CFS does not deteriorate the performance of NMA_CFS due to automation, compared to the original NMA_CFS.


Data clustering feature selection memetic algorithm niching