Citation

Tan, Shing Chiang and Matsumoto, Yoshiyuki and Vasant, Pandian and Junzo, Watada (2017) Rough Set-Based Text Mining from a Large Data Repository of Experts’ Diagnoses for Power Systems. International Conference on Intelligent Decision Technologies, 73 (2). pp. 136-144. ISSN 21903018

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Abstract

Usually it is hard to classify the situation where uncertainty of randomness and fuzziness exists simultaneously. This paper presents a rough set approach applying fuzzy random variable and statistical t-test to text-mine a large data repository of experts’ diagnoses provided by a Japanese power company. The algorithms of rough set and statistical t-test are used to distinguish whether a subset can be classified in the object set or not. The expected-value-approach is also applied to calculate the fuzzy value with probability into a scalar value.

Item Type:	Article
Uncontrolled Keywords:	Fuzzy statistical test,rough set,expected-value-approachrRandomness and fuzziness
Subjects:	Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	15 Mar 2021 21:36
Last Modified:	15 Mar 2021 21:36
URII:	http://shdl.mmu.edu.my/id/eprint/7530

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