Rough set-based text mining from a large data repository of experts� diagnoses for power systems
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 c...
| Main Authors: | Watada, J., Tan, S.C., Matsumoto, Y., Vasant, P. |
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| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.21341 / |
| Published: |
Springer Science and Business Media Deutschland GmbH
2018
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020405285&doi=10.1007%2f978-3-319-59424-8_13&partnerID=40&md5=005bf543c13251000ba83b0cfa00e38d http://eprints.utp.edu.my/21341/ |
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| Summary: |
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. |
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