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Detection of Outlier Information Using Linguistic Summarization

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Flexible Query Answering Systems 2015

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 400))

Abstract

The main goal of automatic summarization of databases is usually to characterize the collection of data in terms of the dominant information involved. In complement to this task, the present paper shows the use of linguistic summarization for the characterization of databases containing textual records through detection of outlier information involved. The method applies a fuzzy measure of similarity between sentences to the summarization result.Certain level of standadization of textual records is assumed.

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References

  1. Mani, I., Maybury, M.T.: Advances in Automatic Text Summarization. The MIT Press, Cambridge (1999)

    Google Scholar 

  2. Pedrycz, W., Al-Hamouz, R., Morfeq, R., Balamash, A.: The Design of Free Structure Granular Mappings: The Use of the Prniciple of Justifiable Granularity. IEEE Trans. on Cybernetics 43(6), 2105–2113 (2013)

    Article  Google Scholar 

  3. Pedrycz, W.: Granular computing in data mining. In: Last, M., Kandel, A. (eds.) Data Mining and Computational Intelligence. Springer, Singapore (2001)

    Google Scholar 

  4. Lin, T.Y., et al. (eds.): Data mining, rough sets and granular computing. Physica-Verlag, Berlin (2002)

    Google Scholar 

  5. Pedrycz, W., Loia, V.: P-FCM: A proximity-based fuzzy clustering. Fuzzy Sets and Systems 128, 21–41 (2004)

    Article  MathSciNet  Google Scholar 

  6. Hawkins, D.: Identification of Outliers. Chapman and Hall (1980)

    Google Scholar 

  7. Aggarwal, C.: Outlier detection in categorical, text and mixed attribute data. In: Outlier Detection, Spronger Book, pp. 199–223 (2013)

    Google Scholar 

  8. Yager, R.R.: Linguistic summaries as a tool for databases discovery. In: Workshop on Fuzzy Databases System and Information Retrieval, Yokohama, Japa (1995)

    Google Scholar 

  9. Zadeh, L.A.: Fuzzy Sets. Information and Control 8, 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  10. Zadeh, L.A.: Toward a theory of fuzzy systems. In: Kalman, R.E., De Claris, N. (eds.) Aspects of Network and System Theory. Holt. Rinehart and Winston. New York (1971)

    Google Scholar 

  11. Niewiadomski, A.: Appliance of fuzzy relations for text documents comparing. In: Proceedings of the 5th Conference on Neural Networks and Soft Computing, Zakopane, Poland, pp. 347–352 (2002)

    Google Scholar 

  12. Niewiadomski, A.: Metods for the Linguistic Summarization of Data: Applications of Fuzzy Sets, EXIT, Warszawa (2008)

    Google Scholar 

  13. Szczepaniak, P.S., Niewiadomski, A.: Internet search based on text intuitionistic fuzzy similarity. In: Szczepaniak, P.S., Segovia, J., Kacprzyk, J., Zadeh, L. (eds.) Intelligent Exploration of the Web. Physica-Verlag. A Springer-Verlag Company, Heidelberg, New York (2003)

    Chapter  Google Scholar 

  14. Delgado, M., Riuz, M., Sanchez, M.D., Vila, M.A.: Fuzzy quantification: a state of the art. Fuzzy Sets and Systems 242, 1–30 (2014)

    Article  MathSciNet  Google Scholar 

  15. Ramos Soto, A.: Linguistic Descriptions for Automatic Generation of textual Short-Therm Weather Forecast on Real Prediction Data. IEEE Transaction on Fuzzy Systems 1–1, 1–13 (2014)

    Google Scholar 

  16. Kacprzyk, J., Zadrozny, S.: Computing with words is an implementable paradigm: fuzzy queries, linguistic data summaries, and natural-language generation. IEEE Transactions on Fuzzy Systems (2010)

    Google Scholar 

  17. Kacprzyk, J., Zadrożny, S.: Bipolar queries: Some inspirations from intention and preference modeling. In: Trillas, E., Bonissone, P.P., Magdalena, L., Kacprzyk, J. (eds.) Combining Experimentation and Theory. STUDFUZZ, vol. 271, pp. 191–208. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  18. Kacprzyk, J., Wilbik, A., Zadrozny, S.: An approach to the liguistic summarization of the series using a fuzzy quantifier driven aggregation. International Journal of Intelligent Systems 25(5), 411–439 (2010)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Information Technology, Lodz University of Technology, ul. Wolczanska 215, 90-924, Lodz, Poland

    Agnieszka Duraj, Piotr S. Szczepaniak & Joanna Ochelska-Mierzejewska

Authors
  1. Agnieszka Duraj
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  2. Piotr S. Szczepaniak
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  3. Joanna Ochelska-Mierzejewska
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Corresponding author

Correspondence to Joanna Ochelska-Mierzejewska .

Editor information

Editors and Affiliations

  1. Dept. of Comm., Business & Info. Tech., Roskilde University, Roskilde, Denmark

    Troels Andreasen

  2. Dept. Computer Science, Roskilde University, Roskilde, Denmark

    Henning Christiansen

  3. Sys Res Intit of Polish Acad of Science, Intelligent Systems Laboratory, Warsaw, Poland

    Janusz Kacprzyk

  4. Department of Electronic Systems, Aalborg University, Esbjerg, Denmark

    Henrik Larsen

  5. Computer Science Department, University Milano-Biccoca, Milano, Italy

    Gabriella Pasi

  6. IRISA, University of Rennes I, Rennes, France

    Olivier Pivert

  7. Dept. of Tele. & Info. Processing, Ghent University, Gent, Belgium

    Guy De Tré

  8. Dept. of Comp. & Art. Int. Sci., University of Granada, Granada, Spain

    Maria Amparo Vila

  9. Department of Computer Engineering, Middle East Technical University, Ankara, Turkey

    Adnan Yazici

  10. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Sławomir Zadrożny

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Duraj, A., Szczepaniak, P.S., Ochelska-Mierzejewska, J. (2016). Detection of Outlier Information Using Linguistic Summarization. In: Andreasen, T., et al. Flexible Query Answering Systems 2015. Advances in Intelligent Systems and Computing, vol 400. Springer, Cham. https://doi.org/10.1007/978-3-319-26154-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-26154-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26153-9

  • Online ISBN: 978-3-319-26154-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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