KFU. Card publication. A technique to aggregate classes of analog fault diagnostic data based on association rule mining

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

A TECHNIQUE TO AGGREGATE CLASSES OF ANALOG FAULT DIAGNOSTIC DATA BASED ON ASSOCIATION RULE MINING

Form of presentation

Articles in international journals and collections

Year of publication

2018

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Authors, employees of KFU

Mosin Sergey Gennadevich, author

Bibliographic description in the original language

Dautov R, Mosin S., A technique to aggregate classes of analog fault diagnostic data based on association rule mining//Proceedings - International Symposium on Quality Electronic Design, ISQED. - 2018. - Vol.2018-March, Is.. - P.238-243.

Keywords

Association Rule Mining, fault diagnostics, analog circuits, AI, ML

The name of the journal

Proceedings - International Symposium on Quality Electronic Design, ISQED

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047952988&doi=10.1109%2fISQED.2018.8357294&partnerID=40&md5=58c44b1d7cff91180cc1a12b3b311e33

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=183208&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Mosin Sergey Gennadevich	ru_RU
dc.date.accessioned	2018-01-01T00:00:00Z	ru_RU
dc.date.available	2018-01-01T00:00:00Z	ru_RU
dc.date.issued	2018	ru_RU
dc.identifier.citation	Dautov R, Mosin S., A technique to aggregate classes of analog fault diagnostic data based on association rule mining//Proceedings - International Symposium on Quality Electronic Design, ISQED. - 2018. - Vol.2018-March, Is.. - P.238-243.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=183208&p_lang=2	ru_RU
dc.description.abstract	Proceedings - International Symposium on Quality Electronic Design, ISQED	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Association Rule Mining	ru_RU
dc.subject	fault diagnostics	ru_RU
dc.subject	analog circuits	ru_RU
dc.subject	AI	ru_RU
dc.subject	ML	ru_RU
dc.title	A technique to aggregate classes of analog fault diagnostic data based on association rule mining	ru_RU
dc.type	Articles in international journals and collections	ru_RU