Skip to main content
Erschienen in: Neural Computing and Applications 7-8/2013

01.06.2013 | ICONIP 2011

Proximity multi-sphere support vector clustering

verfasst von: Trung Le, Dat Tran, Phuoc Nguyen, Wanli Ma, Dharmendra Sharma

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2013

Einloggen

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

search-config
loading …

Abstract

Support vector data description constructs an optimal hypersphere in feature space as a description of a data set. This hypersphere when mapped back to input space becomes a set of contours, and support vector clustering (SVC) employs these contours as cluster boundaries to detect clusters in the data set. However real-world data sets may have some distinctive distributions and hence a single hypersphere cannot be the best description. As a result, the set of contours in input space does not always detect all clusters in the data set. Another issue in SVC is that in some cases, it cannot preserve proximity notation which is crucial for cluster analysis, that is, two data points that are close to each other can be assigned to different clusters using cluster labelling method of SVC. To overcome these drawbacks, we propose Proximity Multi-sphere Support Vector Clustering which employs a set of hyperspheres to provide a better data description for data sets having distinctive distributions and a proximity graph to favour the proximity notation. Experimental results on different data sets are presented to evaluate the proposed clustering technique and compare it with SVC and other clustering techniques.

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!

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+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!

Literatur
1.
Zurück zum Zitat Ben-Hur A, Horn D, Siegelmann H, Vapnik V (2001) Support vector clustering. J Mach Learn Res 2:125–137 Ben-Hur A, Horn D, Siegelmann H, Vapnik V (2001) Support vector clustering. J Mach Learn Res 2:125–137
2.
Zurück zum Zitat Ben-Hur A, Horn D, Siegelmann HT, Vapnik V (2001) A support vector method for hierarchical clustering. In: Advances in neural information processing systems 13. MIT Press, Cambridge, MA, pp 367–373 Ben-Hur A, Horn D, Siegelmann HT, Vapnik V (2001) A support vector method for hierarchical clustering. In: Advances in neural information processing systems 13. MIT Press, Cambridge, MA, pp 367–373
3.
Zurück zum Zitat Bezdek JC (1993) A review of probabilistic, fuzzy and neural models for pattern recognition. J Intell Fuzzy Syst 1(1):1–25MathSciNet Bezdek JC (1993) A review of probabilistic, fuzzy and neural models for pattern recognition. J Intell Fuzzy Syst 1(1):1–25MathSciNet
4.
Zurück zum Zitat Blatt M, Wiseman S, Domany E (1997) Data clustering using a model granular magnet. Neural Comput 9:1805–1842CrossRef Blatt M, Wiseman S, Domany E (1997) Data clustering using a model granular magnet. Neural Comput 9:1805–1842CrossRef
5.
Zurück zum Zitat Estivill-Castro V, Lee I, Murray A (2001) Criteria on proximity graphs for boundary extraction and spatial clustering. In: Proceedings of the 5th Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, pp 348–357 Estivill-Castro V, Lee I, Murray A (2001) Criteria on proximity graphs for boundary extraction and spatial clustering. In: Proceedings of the 5th Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, pp 348–357
6.
Zurück zum Zitat Fukunaga K (1990) Introduction to statistical pattern recognition, second edition (computer science and scientific computing series), 2nd edn. Academic Press, London Fukunaga K (1990) Introduction to statistical pattern recognition, second edition (computer science and scientific computing series), 2nd edn. Academic Press, London
7.
8.
Zurück zum Zitat Kohonen T, Schroeder MR, Huang TS (2001) Self-organizing maps, 3rd edn. Springer, New YorkCrossRef Kohonen T, Schroeder MR, Huang TS (2001) Self-organizing maps, 3rd edn. Springer, New YorkCrossRef
9.
Zurück zum Zitat Le T, Tran D, Ma W, Sharma D (2010) A theoretical framework for multi-sphere support vector data description. In: Proceedings of the 17th international conference on neural information processing: models and applications. Springer, Sydney, pp 132–142 Le T, Tran D, Ma W, Sharma D (2010) A theoretical framework for multi-sphere support vector data description. In: Proceedings of the 17th international conference on neural information processing: models and applications. Springer, Sydney, pp 132–142
10.
Zurück zum Zitat Le T, Tran D, Nguyen P, Ma W, Sharma D (2011) Multiple distribution data description learning method for novelty detection. In: Neural Networks (IJCNN), The 2011 international joint conference on, pp 2321–2326 Le T, Tran D, Nguyen P, Ma W, Sharma D (2011) Multiple distribution data description learning method for novelty detection. In: Neural Networks (IJCNN), The 2011 international joint conference on, pp 2321–2326
11.
Zurück zum Zitat Macqueen JB (1967) Some methods of classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, pp 281–297 Macqueen JB (1967) Some methods of classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, pp 281–297
12.
Zurück zum Zitat Nguyen N, Caruana R (2007) Consensus clustering. In: International conference on data mining Nguyen N, Caruana R (2007) Consensus clustering. In: International conference on data mining
13.
Zurück zum Zitat Roberts S (1997) Parametric and non-parametric unsupervised cluster analysis. Pattern Recogn 30:261–272CrossRef Roberts S (1997) Parametric and non-parametric unsupervised cluster analysis. Pattern Recogn 30:261–272CrossRef
14.
Zurück zum Zitat Rose K, Gurewitz E, Fox GC (1992) Vector quantization by deterministic annealing. IEEE Trans Inf Theory 38(4):1249–1257MATHCrossRef Rose K, Gurewitz E, Fox GC (1992) Vector quantization by deterministic annealing. IEEE Trans Inf Theory 38(4):1249–1257MATHCrossRef
15.
Zurück zum Zitat Shamir R, Sharan R (2000) Click: A clustering algorithm for gene expression analysis. AAAI Press, Menlo Park, CA Shamir R, Sharan R (2000) Click: A clustering algorithm for gene expression analysis. AAAI Press, Menlo Park, CA
16.
Zurück zum Zitat Shamir R, Sharan R (2001) Algorithmic approaches to clustering gene expression data. In: Current topics in computational biology. MIT Press, Cambridge MA, pp 269–300 Shamir R, Sharan R (2001) Algorithmic approaches to clustering gene expression data. In: Current topics in computational biology. MIT Press, Cambridge MA, pp 269–300
17.
Zurück zum Zitat Tamayo P, Donna S, Jill M, Qing Z, Sutisak K, Ethan D, SL E, RG T (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. In: Proceedings of the National Academy of Sciences of the United States of America, vol 96, pp 2907–2912 Tamayo P, Donna S, Jill M, Qing Z, Sutisak K, Ethan D, SL E, RG T (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. In: Proceedings of the National Academy of Sciences of the United States of America, vol 96, pp 2907–2912
18.
Zurück zum Zitat Tax D, Duin R (1998) Outlier detection using classifier instability Tax D, Duin R (1998) Outlier detection using classifier instability
19.
Zurück zum Zitat Tax D, Duin R (1999) Support vector domain description. Pattern Recogn Lett 20:1191–1199CrossRef Tax D, Duin R (1999) Support vector domain description. Pattern Recogn Lett 20:1191–1199CrossRef
20.
Zurück zum Zitat Tax DMJ, Duin RPW (2004) Support vector data description. J Mach Learn Res 54(1):45–66MATHCrossRef Tax DMJ, Duin RPW (2004) Support vector data description. J Mach Learn Res 54(1):45–66MATHCrossRef
21.
Zurück zum Zitat Tran D, Wagner M (2000) Fuzzy entropy clustering. In: FUZZ-IEEE, vol 1, pp 152–157 Tran D, Wagner M (2000) Fuzzy entropy clustering. In: FUZZ-IEEE, vol 1, pp 152–157
22.
Zurück zum Zitat Yang J, Estivill-Castro V, Chalup S (2009) Support vector clustering through proximity graph modelling. In: International conference on neural information processing, vol 2 Yang J, Estivill-Castro V, Chalup S (2009) Support vector clustering through proximity graph modelling. In: International conference on neural information processing, vol 2
Metadaten
Titel
Proximity multi-sphere support vector clustering
verfasst von
Trung Le
Dat Tran
Phuoc Nguyen
Wanli Ma
Dharmendra Sharma
Publikationsdatum
01.06.2013
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 7-8/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-1001-7

Weitere Artikel der Ausgabe 7-8/2013

Neural Computing and Applications 7-8/2013 Zur Ausgabe