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
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 3/2010

01.04.2010 | Original Article

Visualization and clustering of categorical data with probabilistic self-organizing map

verfasst von: Mustapha Lebbah, Khalid Benabdeslem

Erschienen in: Neural Computing and Applications | Ausgabe 3/2010

Einloggen

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

search-config
loading …

Abstract

This paper introduces a self-organizing map dedicated to clustering, analysis and visualization of categorical data. Usually, when dealing with categorical data, topological maps use an encoding stage: categorical data are changed into numerical vectors and traditional numerical algorithms (SOM) are run. In the present paper, we propose a novel probabilistic formalism of Kohonen map dedicated to categorical data where neurons are represented by probability tables. We do not need to use any coding to encode variables. We evaluate the effectiveness of our model in four examples using real data. Our experiments show that our model provides a good quality of results when dealing with categorical data.
Vorheriger Artikel An adaptive wavelet network for function learning
Nächster Artikel A comparison of binless spike train measures

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

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

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+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
Literatur
1.
Andreopoulos B, An A, Wang X (2006) Bi-level clustering of mixed categorical and numerical biomedical data. Int J Data Min Bioinform 1(1):19–56
2.
Anouar F, Badran F, Thiria S (1998) Probabilistic self-organizing map and radial basis function networks. Neurocomputing 20(1–3):83–96MATHCrossRef
3.
4.
Bishop CM, Tipping ME (1998) A hierarchical latent variable model for data visualization. IEEE Trans Pattern Anal Machine Intell 20:281–293CrossRef
5.
Bishop CM, Svensén M, Williams CKI (1998) GTM: the generative topographic mapping. Neural Comput 10(1)
6.
Celeux G, Govaert G (1992) A classification EM algorithm for clustering and stochastic version. Comput Stat Data Anal 14:351–332CrossRefMathSciNet
7.
Celeux G, Forbes F, Payrard N (2003) EM procedures using mean field-like approximations for Markov model-based image segmentation. Pattern Recognit 36:131–144MATHCrossRef
8.
Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J Roy Stat Soc 39(1):1–38MATHMathSciNet
9.
Dolinicar S, Weingessel A, Buchta C, Dimitriadou E (1998) A Comparaison of several cluster algorithms on artificial binary data, scenarios from travel market segmentation. Working paper series 19, SFB (adaptive information systems and modelling in economics and management science)
10.
11.
Girolami M (2001) The topographic organisation and visualisation of binary data using multivariate-Bernoulli latent variable models. IEEE Trans Neural Netw 12(6):1367–1374CrossRef
12.
Graepel T, Burger M, Obermayer K (1998) Self-organizing maps: generalizations and new optimization techniques. Neurocomputing 21:173–190MATHCrossRef
13.
Heskes T (2001) Self-organizing maps, vector quantization, and mixture modeling. IEEE Trans Neural Netw 12:1299–1305CrossRef
14.
Hofmann T (2001) Unsupervised learning by probabilistic latent semantic analysis. Machine Learn 42:177–196MATHCrossRef
15.
Hsu C-C, Wang K-M, Wang S-H (2006) Gvisom for multivariate mixed data projection and structure visualization. In: International joint conferences on neural networks. IJCNN 16–21 July 2006
16.
Ibbou S, Cottrell M (1995) Multiple correspondance analysis crosstabulation matrix using the Kohonen algorithm. In: Verlaeysen M (ed) Proceedings of ESANN’95, pp 27–32. Dfacto Bruxelles
17.
Jollois F, Nadif M (2007) Speed-up for the expectation-maximization algorithm for clustering categorical data. J Glob Optim 37(4):513–525MATHCrossRefMathSciNet
18.
Jorgensen M, Hunt L (1996) Mixture model clustering of data sets with categorical and continuous variables. In: Dove DL, Korb KB, Oliver JJ (eds) Information, statistics and induction in science, ISIS 96, Australia. MIT Press, Cambridge, pp 375–384
19.
Kaban A, Girolami M (2001) A combined latent class and trait model for the analysis and visualization of discrete data. IEEE Trans Pattern Anal Mach Intell 23:859–872CrossRef
20.
Kaski S, Honkela T, Lagus K, Kohonen T (1998) WEBSOM–self-organizing maps of document collections. Neurocomputing 21:101–117MATHCrossRef
21.
Kohonen T (2001) Self-organizing maps. Springer, Berlin.MATH
22.
Kohonen T, Kaski S, Lappalainen H (1997) Self-organized formation of various invariant-feature filters in the adaptive subspace SOM. Neural Comput 9(6):1321–1344CrossRef
23.
Kostiainen T, Lampinen J (2002) On the generative probability density model in the self-organizing map. Neurocomputing 48:217-228MATHCrossRef
24.
Lebart L, Piron M, Steiner J-F (2003) La sémiométrie. Dunod, Paris.
25.
Lebbah M, Thiria S, Badran F (2000) Topological map for binary data, topological map for binary data, ESANN, Bruges, April 26-27-28, (2000), Proceedings.
26.
Lebbah M, Chazottes A, Badran F, Thiria S (2005) Mixed topological map. In: ESANN, pp 357–362
27.
Lebbah M, Rogovschi N, Bennani Y (2007) BeSOM: bernoulli on self organizing map. In: International joint conferences on neural networks. IJCNN 2007, 12–17 August. Orlando, Florida, pp 631–636
28.
Leich F, Weingessel A, Dimitriadou E (1998) E.: Competitive learning for binary data. In: Proceedings of ICANN’98, 2–4 september. Springer, Heidelberg
29.
Luttrel SP (1994) A bayesian analysis of self-organizing maps. Neural Comput 6
30.
Martinetz T, Schulten K (1991) A “neural-gas” network learns topologies. Artif Neural Netw I:397–402
31.
McLachlan G, Krishman T (1997) The EM algorithm and extensions. Wiley, New YorkMATH
32.
Nadif M, Govaert G (1998) Clustering for binary data and mixture models: choice of the model. Appl Stoch Models Data Anal 13:269–278CrossRef
33.
Saund E (1995) A multiple cause mixture model for unsupervised learning. Neural Comput 7:51–71CrossRef
34.
Steiner J-F, Auliard O (1992) La sémiometrie: un outil de validation des réponses. In: Lebart L (ed) La Qualité de l’Information dans les Enquêtes. Quality of information in sample surveys. ASU, Dunod, Paris, pp 241–274
35.
Tipping ME (1999) Probabilistic visualisation of high-dimensional binary data. In: Proceedings of the 1998 conference on advances in neural information processing systems II. MIT Press, Cambridge, pp 592–598. ISBN: 0-262-11245-0
36.
Verbeek JJ, Vlassis N, Kröse BJA (2005) Self-organizing mixture models. Neurocomputing 63:99–123CrossRef
37.
Yin J, Tan Z (2005) Clustering mixed type attributes in large dataset. In: ISPA, pp 655–661
Metadaten
Titel
Visualization and clustering of categorical data with probabilistic self-organizing map
verfasst von
Mustapha Lebbah
Khalid Benabdeslem
Publikationsdatum
01.04.2010
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 3/2010
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-009-0299-2

Weitere Artikel der Ausgabe 3/2010

Neural Computing and Applications 3/2010 Zur Ausgabe

Original Article

A comparison of binless spike train measures

Original Article

Neural network-based simulation for response identification of two-storey shear building subject to earthquake motion

Original Paper

Model reference controlled separately excited DC motor

Original Article

Assessment of porosity using spatial correlation-based radial basis function and neuro-fuzzy inference system

Original Article

An evolving neural network to perform dynamic principal component analysis

Original Article

Feature space versus empirical kernel map and row kernel space in SVMs

Premium Partner

    Bildnachweise