Skip to main content

2019 | OriginalPaper | Buchkapitel

Fuzzy Clustering High-Dimensional Data Using Information Weighting

verfasst von : Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Sergii V. Mashtalir

Erschienen in: Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The fuzzy clustering algorithm for high-dimensional data is proposed in this paper. An objective function which is insensitive to the “concentration of norms” phenomenon is also introduced. We recommend using a weighted parameter in the objective function. The proposed fuzzy clustering algorithm is compared with FCM in the experimental part. Dependence of the clustering algorithm’s results on the weighted parameter changes has also been investigated and tested.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
2.
Zurück zum Zitat Mumford, C.L., Jain, L.C.: Computational Intelligence. Springer, Berlin (2009)CrossRef Mumford, C.L., Jain, L.C.: Computational Intelligence. Springer, Berlin (2009)CrossRef
4.
Zurück zum Zitat Larose, D.T.: Discovering Knowledge in Data: An Introduction to Data Mining. Wiley, Hoboken (2014)MATH Larose, D.T.: Discovering Knowledge in Data: An Introduction to Data Mining. Wiley, Hoboken (2014)MATH
5.
Zurück zum Zitat Gan, G., Ma, Ch., Wu, J.: Data Clustering: Theory, Algorithms, and Application. SIAM, Philadelphia (2007)CrossRef Gan, G., Ma, Ch., Wu, J.: Data Clustering: Theory, Algorithms, and Application. SIAM, Philadelphia (2007)CrossRef
6.
Zurück zum Zitat Aggarwal, C.C., Reddy, C.K.: Data Clustering: Algorithms and Applications. CRC Press, Boca Raton (2014)CrossRef Aggarwal, C.C., Reddy, C.K.: Data Clustering: Algorithms and Applications. CRC Press, Boca Raton (2014)CrossRef
7.
Zurück zum Zitat Yang, M.-S., Chang-Chien, S.-J., Hung, W.-L.: An unsupervised clustering algorithm for data on the unit hypersphere. Appl. Soft Comput. 42, 290–313 (2016)CrossRef Yang, M.-S., Chang-Chien, S.-J., Hung, W.-L.: An unsupervised clustering algorithm for data on the unit hypersphere. Appl. Soft Comput. 42, 290–313 (2016)CrossRef
8.
Zurück zum Zitat Gosain, A., Dahiya, S.: Performance analysis of various fuzzy clustering algorithms: a review. Procedia Comput. Sci. 79, 100–111 (2016)CrossRef Gosain, A., Dahiya, S.: Performance analysis of various fuzzy clustering algorithms: a review. Procedia Comput. Sci. 79, 100–111 (2016)CrossRef
9.
Zurück zum Zitat Xu, R., Wunsch, D.C.: Clustering. IEEE Press Series on Computational Intelligence. Wiley, Hoboken (2009) Xu, R., Wunsch, D.C.: Clustering. IEEE Press Series on Computational Intelligence. Wiley, Hoboken (2009)
10.
Zurück zum Zitat Babichev, S., Lytvynenko, V., Korobchynskyi, M., Taiff, M.A.: Objective clustering inductive technology of gene expression sequences features. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2017. CCIS, vol. 716, pp. 359–372. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58274-0_29CrossRef Babichev, S., Lytvynenko, V., Korobchynskyi, M., Taiff, M.A.: Objective clustering inductive technology of gene expression sequences features. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2017. CCIS, vol. 716, pp. 359–372. Springer, Cham (2017). https://​doi.​org/​10.​1007/​978-3-319-58274-0_​29CrossRef
12.
Zurück zum Zitat Hu, Z., Bodyanskiy, Ye.V., Tyshchenko, O.K.: A cascade deep neuro-fuzzy system for high-dimensional online possibilistic fuzzy clustering. In: Proceedings of the 11th International Scientific and Technical Conference “Computer Sciences and Information Technologies”, CSIT 2016, Lviv, pp. 119–122 (2016) Hu, Z., Bodyanskiy, Ye.V., Tyshchenko, O.K.: A cascade deep neuro-fuzzy system for high-dimensional online possibilistic fuzzy clustering. In: Proceedings of the 11th International Scientific and Technical Conference “Computer Sciences and Information Technologies”, CSIT 2016, Lviv, pp. 119–122 (2016)
13.
Zurück zum Zitat Hu, Z., Bodyanskiy, Ye.V., Tyshchenko, O.K., Boiko, O.O.: A neuro-fuzzy Kohonen network for data stream possibilistic clustering and its online self-learning procedure. Appl. Soft Comput. J. 68, 710–718 (2018)CrossRef Hu, Z., Bodyanskiy, Ye.V., Tyshchenko, O.K., Boiko, O.O.: A neuro-fuzzy Kohonen network for data stream possibilistic clustering and its online self-learning procedure. Appl. Soft Comput. J. 68, 710–718 (2018)CrossRef
14.
Zurück zum Zitat Bodyanskiy, Ye.V., Tyshchenko, O.K., Kopaliani, D.S.: An evolving connectionist system for data stream fuzzy clustering and its online learning. Neurocomputing 262, 41–56 (2017)CrossRef Bodyanskiy, Ye.V., Tyshchenko, O.K., Kopaliani, D.S.: An evolving connectionist system for data stream fuzzy clustering and its online learning. Neurocomputing 262, 41–56 (2017)CrossRef
17.
Zurück zum Zitat Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)CrossRef Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)CrossRef
18.
Zurück zum Zitat Hoeppner, F., Klawonn, F., Kruse, R., Runkler, T.: Fuzzy Clustering Analysis: Methods for Classification, Data Analysis, and Image Recognition. Wiley, Chichester (1999)MATH Hoeppner, F., Klawonn, F., Kruse, R., Runkler, T.: Fuzzy Clustering Analysis: Methods for Classification, Data Analysis, and Image Recognition. Wiley, Chichester (1999)MATH
19.
20.
Zurück zum Zitat Bezdek, J.C., Keller, J., Krishnapuram, R., Pal, N.R.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Springer, New York (2005) Bezdek, J.C., Keller, J., Krishnapuram, R., Pal, N.R.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Springer, New York (2005)
21.
Zurück zum Zitat Keller, A., Klawonn, F.: Fuzzy clustering with a weighting of data variables. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 8(6), 735–746 (2000)CrossRef Keller, A., Klawonn, F.: Fuzzy clustering with a weighting of data variables. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 8(6), 735–746 (2000)CrossRef
Metadaten
Titel
Fuzzy Clustering High-Dimensional Data Using Information Weighting
verfasst von
Yevgeniy V. Bodyanskiy
Oleksii K. Tyshchenko
Sergii V. Mashtalir
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-20912-4_36

Premium Partner