Skip to main content
SpringerLink
Log in

Evaluation of Stream Data by Formal Concept Analysis

  • Conference paper
Book cover New Trends in Databases and Information Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 185))

  • 1410 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Carpineto, C., Romano, G.: Concept Data Analysis. Theory and Application. John Wiley & Sons, New York (2004)

    Book  Google Scholar 

  2. Carpineto, C., Romano, G.: Using Concept Lattices for Text Retrieval and Mining. In: Ganter, B., Stumme, G., Wille, R. (eds.) Formal Concept Analysis. LNCS (LNAI), vol. 3626, pp. 161–179. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  3. Franceschet, M.: The Role of Conference Publications in CS. Communications of the ACM 53(12) (December 2010)

    Google Scholar 

  4. Ganter, B.: Two Basic Algorithms in Concept Analysis. In: Kwuida, L., Sertkaya, B. (eds.) ICFCA 2010. LNCS, vol. 5986, pp. 312–340. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  5. Ganter, B., Stumme, G., Wille, R. (eds.): Formal Concept Analysis. LNCS (LNAI), vol. 3626. Springer, Heidelberg (2005)

    Google Scholar 

  6. Ganter, B., Wille, R.: Applied Lattice Theory: Formal Concept Analysis. In: Grätzer, G.A. (ed.) General Lattice Theory, pp. 592–606. Birkhäuser (1997)

    Google Scholar 

  7. Ganter, B., Wille, R.: Formal Concept Analysis – Mathematical Foundations. Springer, Berlin (1999)

    Book  MATH  Google Scholar 

  8. Hald, A.: Statistical Theory of Sampling Inspection by Attributes. Academic Press, London (1981)

    MATH  Google Scholar 

  9. Khosravi, A., Nahavandi, S., Creighton, D.C.: Predicting Amount of Saleable Products Using Neural Network Metamodels of Casthouses. In: ICARCV, pp. 2018–2023. IEEE (2010)

    Google Scholar 

  10. Kuznetsov, S.O.: On Stability of a Formal Concept. Annals of Mathematics and Artificial Intelligence 49(1), 101–115 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  11. van der Merwe, D., Obiedkov, S., Kourie, D.: AddIntent: A New Incremental Algorithm for Constructing Concept Lattices. In: Eklund, P. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 372–385. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Montgomery, D.C.: Introduction to Statistical Quality Control. John Wiley & Sons, New York (1985)

    Google Scholar 

  13. Priss, U.: Formal Concept Analysis in Information Science. Annual Review of Information Science and Technology 40, 521–543 (2006)

    Article  Google Scholar 

  14. Roth, C., Obiedkov, S., Kourie, D.: Towards Concise Representation for Taxonomies of Epistemic Communities. In: Yahia, S.B., Nguifo, E.M., Belohlavek, R. (eds.) CLA 2006. LNCS (LNAI), vol. 4923, pp. 240–255. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  15. Shi, G., Zhou, X., Zhang, G.: The Use of Artificial Neural Network Analysis and Multiple Regression for Trap Quality Evaluation: A Case Study of the Northern Kuqa Depression of Tarim Basin in Western China. Marine and Petroleum Geology 21(3), 411–420 (2004)

    Article  Google Scholar 

  16. Wille, R.: Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts. In: Rival, I. (ed.) Ordered Sets, pp. 445–470. Reidel, Dordrecht–Boston (1982)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. VSB Technical University Ostrava, Ostrava, Czech Republic

    Martin Radvanský & Václav Snášel

  2. Pike Automation s.r.o., Praha, Czech Republic

    Vladimír Sklenář

Authors
  1. Martin Radvanský
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vladimír Sklenář
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Václav Snášel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Martin Radvanský .

Editor information

Editors and Affiliations

  1. , Department of Computer Science, Eindhoven University of Technology, Eindhoven, 5600, Netherlands

    Mykola Pechenizkiy

  2. Institute of Computing Science, Poznan University of Technology, ul. Piotrowo 2, Poznan, 60-965, Poland

    Marek Wojciechowski

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Radvanský, M., Sklenář, V., Snášel, V. (2013). Evaluation of Stream Data by Formal Concept Analysis. In: Pechenizkiy, M., Wojciechowski, M. (eds) New Trends in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol 185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32518-2_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-32518-2_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32517-5

  • Online ISBN: 978-3-642-32518-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation