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Selection of Initial Modes for Rough Possibilistic K-Modes Methods

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Book cover Foundations of Intelligent Systems (ISMIS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10352))

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Abstract

This paper proposes two new improvements of the k-modes algorithm under possibilistic and rough frameworks. These new versions of the k-modes deal with uncertainty in the values of attributes and in the belonging of objects to several clusters and handle rough clusters using possibility and rough set theories. In fact, both of the k-modes under possibilistic framework (KM-PF) and the k-modes using possibility and rough set theories (KM-PR) provide successful results when clustering categorical values of attributes under uncertain frameworks. However, they use a random selection of the initial modes which can have a bad impact on the final results. As a good selection of the initial modes can make better the clustering results, our aim through this paper is to propose new methods that improve the KM-PF and KM-PR methods through the selection of initial modes. Besides, we will study and analyze the impact of the good selection of the initial modes on both KM-PF and KM-PR. The test of the new methods that select the initial modes from the most dissimilar objects shows the improvement made in terms of accuracy, iteration number, and execution time.

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References

  1. Ammar, A., Elouedi, Z., Lingras, P.: The k-modes method using possibility and rough set theories. In: Proceedings of the IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS. IEEE (2013)

    Google Scholar 

  2. Ammar, A., Elouedi, Z., Lingras, P.: The K-modes method under possibilistic framework. In: Zaïane, O.R., Zilles, S. (eds.) AI 2013. LNCS (LNAI), vol. 7884, pp. 211–217. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38457-8_18

    Chapter  Google Scholar 

  3. Dubois, D., Prade, H.: Possibility Theory: An Approach to Computerized Processing of Uncertainty. Plenium Press, New York (1988)

    Book  MATH  Google Scholar 

  4. Jenhani, I., Ben Amor, N., Elouedi, Z., Benferhat, S., Mellouli, K.: Information affinity: a new similarity measure for possibilistic uncertain information. In: Mellouli, K. (ed.) ECSQARU 2007. LNCS (LNAI), vol. 4724, pp. 840–852. Springer, Heidelberg (2007). doi:10.1007/978-3-540-75256-1_73

    Chapter  Google Scholar 

  5. Joshi, M., Lingras, P., Rao, C.R.: Correlating fuzzy and rough clustering. Fundamenta Informaticae 115, 233–246 (2012)

    MathSciNet  MATH  Google Scholar 

  6. Lingras, P., Nimse, S., Darkunde, N., Muley, A.: Soft clustering from crisp clustering using granulation for mobile call mining. In: Proceedings of the GrC 2011: International Conference on Granular Computing, pp. 410–416 (2011)

    Google Scholar 

  7. Murphy, M.P., Aha, D.W.: UCI repository databases (1996) http://www.ics.uci.edu/mlearn

  8. Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining Knowl. Discov. 2, 283–304 (1998)

    Article  Google Scholar 

  9. Huang, Z., Ng, M.K.: A note on k-modes clustering. J. Classif. 20, 257–261 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  10. Pawlak, Z.: Rough sets. Int. J. Inf. Comput. Sci. 11, 341–356 (1982)

    Article  MATH  Google Scholar 

  11. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Berlin (1992)

    MATH  Google Scholar 

  12. Tanaka, H., Guo, P.: Possibilistic Data Analysis for Operations Research. Physica-Verlag, Heidelberg (1999)

    MATH  Google Scholar 

  13. Viattchenin, D.A.: A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications. Springer Publishing Company, Incorporated, Heidelberg (2013)

    Book  MATH  Google Scholar 

  14. Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1, 3–28 (1978)

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. LARODEC, Institut Supérieur de Gestion de Tunis, Université de Tunis, 41 Avenue de la Liberté, 2000, Le Bardo, Tunisia

    Asma Ammar & Zied Elouedi

Authors
  1. Asma Ammar
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  2. Zied Elouedi
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Corresponding author

Correspondence to Asma Ammar .

Editor information

Editors and Affiliations

  1. Warsaw University of Technology, Warsaw, Poland

    Marzena Kryszkiewicz

  2. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  3. Institute of Informatics, University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  4. Faculty of Electronics & Information, Warsaw University of Technology, Warsaw, Poland

    Henryk Rybinski

  5. Institute of Mathematics, Warsaw University, Warsaw, Poland

    Andrzej Skowron

  6. Department of Computer Science, University of North Carolina at Charlotte, North Carolina, USA

    Zbigniew W. Raś

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Ammar, A., Elouedi, Z. (2017). Selection of Initial Modes for Rough Possibilistic K-Modes Methods. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybinski, H., Skowron, A., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2017. Lecture Notes in Computer Science(), vol 10352. Springer, Cham. https://doi.org/10.1007/978-3-319-60438-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-60438-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60437-4

  • Online ISBN: 978-3-319-60438-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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