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Two Rough Set-based Software Tools for Analyzing Non-Deterministic Data

Two Rough Set-based Software Tools for Analyzing Non-Deterministic Data

Mao Wu, Michinori Nakata, Hiroshi Sakai
Copyright: © 2014 |Volume: 1 |Issue: 1 |Pages: 16
ISSN: 2334-4598|EISSN: 2334-4601|EISBN13: 9781466662407|DOI: 10.4018/ijrsda.2014010103
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MLA

Wu, Mao, et al. "Two Rough Set-based Software Tools for Analyzing Non-Deterministic Data." IJRSDA vol.1, no.1 2014: pp.32-47. http://doi.org/10.4018/ijrsda.2014010103

APA

Wu, M., Nakata, M., & Sakai, H. (2014). Two Rough Set-based Software Tools for Analyzing Non-Deterministic Data. International Journal of Rough Sets and Data Analysis (IJRSDA), 1(1), 32-47. http://doi.org/10.4018/ijrsda.2014010103

Chicago

Wu, Mao, Michinori Nakata, and Hiroshi Sakai. "Two Rough Set-based Software Tools for Analyzing Non-Deterministic Data," International Journal of Rough Sets and Data Analysis (IJRSDA) 1, no.1: 32-47. http://doi.org/10.4018/ijrsda.2014010103

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Abstract

Rough Non-deterministic Information Analysis (RNIA) is a rough set-based framework for handling tables with exact and inexact data. Under this framework, the authors have coped with several issues, for example, possible equivalence relations, data dependencies, rule generation, rule stability, question-answering as well as missing and interval values as special cases of non-deterministic values. In this paper, the authors at first survey a software tool in Prolog and C for dealing issues like rule generation and question-answering, then we report the current state of our new web tool called getRNIA. The authors also propose a new issue focusing on the estimation of actual derived DIS from a Non-deterministic Information System, by employing constraints like Equivalence Classes, Data Dependency, Maximum Likelihood Estimation and Data Consistency.

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