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  • Following article presents practical usage of the Formal Concept Analysis (FCA) for the evaluation of stream data recorded during a technological process. The main aim of this paper is to show possibilities of using FCA to detect anomalies in the data. Our attitude is based on the fact that although during the production process a large amount of input data is obtained, the size of conceptual lattice is relatively small, and therefore, it is possible to work with it in real-time. The conceptual lattice represents a model of production process, and this model is based on historical production data. The input data stream contains measurements on the production line and it is applied on the model of the production process. The result of this activity is to identify anomalies in the incoming data and their relationship with faulty products, including disclosure of possible causes of errors and also to obtain a histogram of quality for manufactured products.
  • Following article presents practical usage of the Formal Concept Analysis (FCA) for the evaluation of stream data recorded during a technological process. The main aim of this paper is to show possibilities of using FCA to detect anomalies in the data. Our attitude is based on the fact that although during the production process a large amount of input data is obtained, the size of conceptual lattice is relatively small, and therefore, it is possible to work with it in real-time. The conceptual lattice represents a model of production process, and this model is based on historical production data. The input data stream contains measurements on the production line and it is applied on the model of the production process. The result of this activity is to identify anomalies in the incoming data and their relationship with faulty products, including disclosure of possible causes of errors and also to obtain a histogram of quality for manufactured products. (en)
Title
  • Evaluation of Stream Data by Formal Concept Analysis
  • Evaluation of Stream Data by Formal Concept Analysis (en)
skos:prefLabel
  • Evaluation of Stream Data by Formal Concept Analysis
  • Evaluation of Stream Data by Formal Concept Analysis (en)
skos:notation
  • RIV/61989100:27240/13:86093048!RIV15-MSM-27240___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 73664
http://linked.open...ai/riv/idVysledku
  • RIV/61989100:27240/13:86093048
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • data mining; formal concept analysis; quality of production; stream data (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [714C7E2A8D11]
http://linked.open...v/mistoKonaniAkce
  • Poznaň
http://linked.open...i/riv/mistoVydani
  • Heidelberg
http://linked.open...i/riv/nazevZdroje
  • Advances in Intelligent Systems and Computing
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Snášel, Václav
  • Sklenář, Vladimír
  • Radvanský, Martin
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000312972300013
http://linked.open.../riv/zahajeniAkce
issn
  • 2194-5357
number of pages
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 978-3-642-32517-5
http://localhost/t...ganizacniJednotka
  • 27240
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