Rough set-based text mining from a large data repository of experts� diagnoses for power systems

Watada, J. and Tan, S.C. and Matsumoto, Y. and Vasant, P. (2018) Rough set-based text mining from a large data repository of experts� diagnoses for power systems. Smart Innovation, Systems and Technologies, 73. pp. 136-144.

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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. © Springer International Publishing AG 2018.

Item Type: Article
Impact Factor: cited By 0; Conference of 9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017 ; Conference Date: 21 June 2017 Through 23 June 2017; Conference Code:192309
Uncontrolled Keywords: Data mining; Electric utilities; Fuzzy set theory; Fuzzy systems; Random processes, Expected values; Fuzzy random variable; Large data; Power company; Randomness and fuzziness; Rough-set based; Scalar values; Text mining, Rough set theory
Departments / MOR / COE: Research Institutes > Institute for Autonomous Systems
Depositing User: Mr Ahmad Suhairi Mohamed Lazim
Date Deposited: 01 Aug 2018 01:09
Last Modified: 20 Feb 2019 01:54
URI: http://scholars.utp.edu.my/id/eprint/21991

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