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Application of Rough Finite State Automata in Decision Making

Published on March 2017 by Swati Gupta, Sunita Garhwal
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Emerging Trends in Computing
Foundation of Computer Science USA
ETC2016 - Number 1
March 2017
Authors: Swati Gupta, Sunita Garhwal
56407b78-dbbd-4942-9937-77c4de33e429

Swati Gupta, Sunita Garhwal . Application of Rough Finite State Automata in Decision Making. Emerging Trends in Computing. ETC2016, 1 (March 2017), 23-28.

@article{
author = { Swati Gupta, Sunita Garhwal },
title = { Application of Rough Finite State Automata in Decision Making },
journal = { Emerging Trends in Computing },
issue_date = { March 2017 },
volume = { ETC2016 },
number = { 1 },
month = { March },
year = { 2017 },
issn = 0975-8887,
pages = { 23-28 },
numpages = 6,
url = { /proceedings/etc2016/number1/27304-6257/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Emerging Trends in Computing
%A Swati Gupta
%A Sunita Garhwal
%T Application of Rough Finite State Automata in Decision Making
%J Emerging Trends in Computing
%@ 0975-8887
%V ETC2016
%N 1
%P 23-28
%D 2017
%I International Journal of Computer Applications
Abstract

RST is a formal scientific tool presented by shine researcher Pawlak [5] as in the early 1980s that oversees powerfully the instability which emerges from incomplete, noisy or inexact data. The rough set hypothesis is an essential method for data mining which incorporates extracting knowledge from a lot of information, finding new patterns, and anticipating the future trends. As of late, Basu [1] outlined a numerical model, named rough finite state automata, which perceives such rough sets and is believed to end up being of awesome significance to the researchers in the field of data analysis in near future. The aim of the paper is to design a RFSA for a rough dataset taken from the UCI machine repository.

References
  1. S. Basu, "Rough Finite-State Automata", Cybernetics and Systems: An InternationalJournal, vol. 36, pp. 107-124, 2005.
  2. A. K. Bag, B. Tudu, N. Bhattacharyya and R. Bandyopadhyay, "Dealing With Redundant Features and Inconsistent Training Data in Electronic Nose: A Rough Set Based Approach", IEEE Sensors Journal, vol. 14(3), March 2014.
  3. S. Sharan, A. K. Srivastava andS. P. Tiwari, "Characterizations of rough finite state automata", International Journal of Machine Learning and Cybernetics, pp. 1-10, May 2015.
  4. D. Parmar, T. Wu and J. Blackhurst, "MMR: An algorithm for clustering categorical data using Rough Set Theory", Data & Knowledge Engineering, vol. 63, pp. 879–893, 2007.
  5. Z. Pawlak, "Rough Sets", International Journal of Computer & Information Sciences,vol. 11(5), pp. 341-356, October 1982.
  6. A. G. Jackson, Z. Pawlak and S. R. LeClair, "Rough sets applied to the discovery of materials knowledge", Journal of Alloys and Compounds, vol. 279(1), pp. 14–21, September 1998.
  7. P. Zhu, "Covering rough sets based on neighborhoods: An approach without using neighborhoods", International Journal of Approximate Reasoning, vol. 52(3), pp. 461–472, March 2011.
  8. A. J. Krener, "Reduced Order Modeling of Nonlinear Control Systems", Analysis and Design of Nonlinear Control Systems, pp. 41-62.
  9. Q. Zhang, J. Wang, G. Wang and H. Yu, "The approximation set of a vague set in rough approximation space", Information Sciences, vol. 300, pp. 1–19, April 2015.
  10. P. Kumar and B. K. Tripathy, "MMeR: an algorithm for clustering heterogeneous data using rough set theory", International Journal of Rapid Manufacturing (IJRAPIDM), vol. 1(2), 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Rough Finite State Automata Rough Finite State Semi Automata Rough Set Rough Set Theory Decision Making