Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Computer Based Stylometric Analysis of Texts in Polish Language Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

A Study of Cluster Validity Indices for Real-Life Data

verfasst von : Artur Starczewski, Adam Krzyżak

Erschienen in: Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

In this paper a study of several cluster validity indices for real-life data sets is presented. Moreover, a new version of validity index is also proposed. All these indices can be considered as a measure of data partitioning accuracy and the performance of them is demonstrated for real-life data sets, where three popular algorithms have been applied as underlying clustering techniques, namely the Complete–linkage, Expectation Maximization and K-means algorithms. The indices have been compared taking into account the number of clusters in a data set. The results are useful to choose the best validity index for a given data set.

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Vorheriges Kapitel Exercise Recognition Using Averaged Hidden Markov Models
Nächstes Kapitel Improvement of the Validity Index for Determination of an Appropriate Data Partitioning
Literatur
1.
Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Pérez, J.M., Perona, I.: An extensive comparative study of cluster validity indices. Pattern Recogn. 46, 243–256 (2013)CrossRef
2.
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)CrossRefMATH
3.
Bilski, J., Smoląg, J.: Parallel architectures for learning the RTRN and Elman dynamic neural networks. IEEE Trans. Parallel Distrib. Syst. 26(9), 2561–2570 (2015)CrossRef
4.
Bilski, J., Wilamowski, B.M.: Parallel learning of feedforward neural networks without error backpropagation. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS, vol. 9692, pp. 57–69. Springer, Cham (2016). doi:10.​1007/​978-3-319-39378-0_​6
5.
Bilski, J., Kowalczyk, B., Żurada, J.M.: Application of the givens rotations in the neural network learning algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS, vol. 9692, pp. 46–56. Springer, Cham (2016). doi:10.​1007/​978-3-319-39378-0_​5
6.
Bradley, P., Fayyad, U.: Refining initial points for k-means clustering. In: Proceedings of the Fifteenth International Conference on Knowledge Discovery and Data Mining, New York, pp. 9–15. AAAI Press (1998)
7.
Cpałka, K., Rebrova, O., Nowicki, R., Rutkowski, L.: On design of flexible neuro-fuzzy systems for nonlinear modelling. Int. J. Gen. Syst. 42(6), 706–720 (2013)CrossRefMATH
8.
Cpałka, K., Rutkowski, L.: Flexible Takagi-Sugeno fuzzy systems. In: Proceedings of the 2005 IEEE International Joint Conference on IJCNN Neural Networks (2005)
9.
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1(2), 224–227 (1979)CrossRef
10.
Duch, W., Korbicz, J., Rutkowski, L., Tadeusiewicz, R. (eds.): Biocybernetics and Biomedical Engineering 2000. Neural Networks, vol. 6. Akademicka Oficyna Wydawnicza EXIT (2000)
11.
12.
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial data sets with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, pp. 226–231 (1996)
13.
Fränti, P., Rezaei, M., Zhao, Q.: Centroid index: cluster level similarity measure. Pattern Recogn. 47(9), 3034–3045 (2014)CrossRef
14.
Gabryel, M.: A bag-of-features algorithm for applications using a NoSQL database. Inf. Softw. Technol. 639, 332–343 (2016)CrossRef
15.
Gabryel, M., Grycuk, R., Korytkowski, M., Holotyak, T.: Image indexing and retrieval using GSOM algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS, vol. 9119, pp. 706–714. Springer, Cham (2015). doi:10.​1007/​978-3-319-19324-3_​63 CrossRef
16.
Guha, S., Rastogi, R., Shim, K.: CURE: an efficient clustering algorithm for large databases. In: Proceedings of the 1998 ACM-SIGMOD International Conference Management of Data (SIGMOD 1998), pp. 73–84 (1998)
17.
Guha, S., Rastogi, R., Shim, K.: ROCK: a robust clustering algorithm for categorical attributes. In: The Proceedings of the IEEE Conference on Data Engineering (1999)
18.
19.
Halkidi, M., Batistakis, Y., Vazirgiannis, M.: Clustering validity checking methods: part II. ACM SIGMOD Record 31(3), 19–27 (2002)CrossRefMATH
20.
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Data Mining, Inference and Prediction. Springer, New York (2001)MATH
21.
Hinneburg, A., Keim, D.A.: An efficient approach to clustering in large multimedia databases with noise. In: Knowledge Discovery and Data Mining (1998)
22.
Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988)MATH
23.
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)CrossRefMATH
24.
Lago-Fernández, L.F., Corbacho, F.: Normality-based validation for crisp clustering. Pattern Recogn. 43(3), 782–795 (2010)CrossRefMATH
25.
26.
Meng, X., van Dyk, D.: The EM algorithm - An old folk-song sung to a fast new tune. J. R. Stat. Soc. Ser. B (Methodol.) 59(3), 511–567 (1997)MathSciNetCrossRefMATH
27.
Murtagh, F.: A survey of recent advances in hierarchical clustering algorithms. Comput. J. 26(4), 354–359 (1983)CrossRefMATH
28.
Pakhira, M.K., Bandyopadhyay, S., Maulik, U.: Validity index for crisp and fuzzy clusters. Pattern Recogn. 37(3), 487–501 (2004)CrossRefMATH
29.
Pal, N.R., Bezdek, J.C.: On cluster validity for the fuzzy c-means model. IEEE Trans. Fuzzy Syst. 3(3), 370–379 (1995)CrossRef
30.
Pascual, D., Pla, F., Sánchez, J.S.: Cluster validation using information stability measures. Pattern Recogn. Lett. 31(6), 454–461 (2010)CrossRef
31.
Pelleg, D., Moore, A.W.: X-means: extending k-means with efficient estimation of the number of clusters. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 727–734 (2000)
32.
Rohlf, F.: Single-link clustering algorithms. In: Krishnaiah, P.R., Kanal, L.N. (eds.) Handbook of Statistics, vol. 2, pp. 267–284 (1982)
33.
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)CrossRefMATH
34.
Rutkowski, L., Cpałka, K.: Compromise approach to neuro-fuzzy systems. In: Sincak, P., Vascak, J., Kvasnicka, V., Pospichal, J. (eds.) Intelligent Technologies - Theory and Applications. New Trends in Intelligent Technologies. Frontiers in Artificial Intelligence and Applications, vol. 76, pp. 85–90 (2002)
35.
Rutkowski, L., Przybył, A., Cpałka, K., Er, M.J.: Online speed profile generation for industrial machine tool based on neuro-fuzzy approach. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS, vol. 6114, pp. 645–650. Springer, Heidelberg (2010). doi:10.​1007/​978-3-642-13232-2_​79 CrossRef
36.
Rutkowski, L., Cpałka, K.: A neuro-fuzzy controller with a compromise fuzzy reasoning. Control Cybern. 31(2), 297–308 (2002)MATH
37.
Saha, S., Bandyopadhyay, S.: Some connectivity based cluster validity indices. Appl. Soft Comput. 12(5), 1555–1565 (2012)CrossRef
38.
Sheikholeslami, G., Chatterjee, S., Zhang, A.: Wave cluster: a multiresolution clustering approach for very large spatial databases. In: Proceedings of the 1998 International Conference on Very Large Data Bases (VLDB 1998), pp. 428–439 (1998)
39.
Shieh, H.-L.: Robust validity index for a modified subtractive clustering algorithm. Appl. Soft Comput. 22, 47–59 (2014)CrossRef
40.
41.
Starczewski, A., Krzyżak, A.: A modification of the silhouette index for the improvement of cluster validity assessment. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS, vol. 9693, pp. 114–124. Springer, Cham (2016). doi:10.​1007/​978-3-319-39384-1_​10
42.
Wang, W., Yang, J., Muntz, M.: STING: a statistical information grid approach to spatial data mining. In: Proceedings of the 1997 International Conference on Very Large Data Bases (VLDB 1997), pp. 186–195 (1997)
43.
44.
Zhao, Q., Fränti, P.: WB-index: a sum-of-squares based index for cluster validity. Data Knowl. Eng. 92, 77–89 (2014)CrossRef
45.
Zhang, T., Ramakrishnan, R., Linvy, M.: BIRCH: an efficient data clustering method for very large data sets. Data Min. Knowl. Discov. 1(2), 141–182 (1997)CrossRef
Metadaten
Titel
A Study of Cluster Validity Indices for Real-Life Data
verfasst von
Artur Starczewski
Adam Krzyżak
Copyright-Jahr
2017
Buch
Artificial Intelligence and Soft Computing

Print ISBN: 978-3-319-59059-2

Electronic ISBN: 978-3-319-59060-8

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-59060-8_15

Premium Partner

    Bildnachweise