Uncertainty in Data Mining

Ayad Imam, Rafid Allawi

Abstract

This paper aims to articulate the current approaches to handle the uncertainty problem in data mining (DM). The difficulties in DM are given to show the impact of the uncertainty problem in DM’s various applications. As it has been stated that is no common DM method to handle the uncertainty problem in DM and based upon the literature of cognitive science, this paper highlights a new classification approach to overcome the uncertainty problem in DM that is the Relative-Fuzzy (RF) approach and its ML/RFL-Based Net software tool.

Download


Paper Citation


in Harvard Style

Imam A. and Allawi R. (2020). Uncertainty in Data Mining.In Proceedings of the 1st International Conference on Computing and Emerging Sciences - Volume 1: ICCES, ISBN 978-989-758-497-8, pages 74-78. DOI: 10.5220/0010461400740078


in Bibtex Style

@conference{icces20,
author={Ayad Imam and Rafid Allawi},
title={Uncertainty in Data Mining},
booktitle={Proceedings of the 1st International Conference on Computing and Emerging Sciences - Volume 1: ICCES,},
year={2020},
pages={74-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010461400740078},
isbn={978-989-758-497-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Computing and Emerging Sciences - Volume 1: ICCES,
TI - Uncertainty in Data Mining
SN - 978-989-758-497-8
AU - Imam A.
AU - Allawi R.
PY - 2020
SP - 74
EP - 78
DO - 10.5220/0010461400740078