Abstract—Fuzzy rough data reduction algorithm proposed in [1] is not convergent on higher dimensional data due to its computational complexity increases exponentially as the number of input attributes and fuzzy sets increase. This paper shows how singular value decomposition can be used as a useful preprocessing method in order to achieve fuzzy rough reduct convergence on higher dimensional datasets. Eight datasets from UCI repository have been taken for the experimentation.

Index Terms—Ant olony ptimization (ACO), Computational Complexity, Fuzzification, Fuzzy-Rough Reduct, Singular Value Decomposition (SVD).

Y Ramadevi is a Professor in Dept. of CSE, Chaitanya Bharathi Institute of Technology, Hyderabad, India. (e-mail:yrdcse.cbit@gmail.com)
P Venugopal is a research associate with the Dr YRamadevi.(e-mail:venu062k5@gmail.com).
PSVS Sai Prasad is Assistant Professor in the Dept. of Computers and Information Sciences, University of Hyderabad, Hyderabad, India.(e-mail: saics@uohyd.ernet.in).

Cite: Rama Devi Y, Venu Gopal P, and Sai Prasad PSVS, "Fuzzy Rough Data Reduction Using SVD," International Journal of Computer and Electrical Engineering vol. 3, no. 3, pp. 384-388, 2011.