Incremental unsupervised time series analysis using merge growing neural gas

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5629 LNCS, Issue , 2009, Pages 10-18

  Andreakis, Andreas ^a   Hoyningen Huene, Nicolai V ^a   Beetz, Michael ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Incremental; Recursive temporal context; Self organizing; Time series analysis; Unsupervised

Indexed keywords

APRIORI; CONSTANT PARAMETERS; ENTROPY MAXIMIZATION; FREQUENT SEQUENCES; GROWING NEURAL GAS; INCREMENTAL; INFINITE TIME; NEURAL GAS; RECURSIVE TEMPORAL CONTEXT; SELF-ORGANIZING; TIME COMPLEXITY; UNSUPERVISED;

CONFORMAL MAPPING; MERGERS AND ACQUISITIONS; NEURONS; RECURSIVE FUNCTIONS; SELF ORGANIZING MAPS; TIME SERIES;

TIME SERIES ANALYSIS;

EID: 69049106545     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02397-2_2     Document Type: Conference Paper

References (26)

1. Strickert, M., Hammer, B.: Merge SOM for temporal data. Neurocomputing 64, 39-71 (2005)
2. Fritzke, B.: A Growing Neural Gas network learns topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) NIPS, pp. 625-632. MIT Press, Cambridge (1995)
3. Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Berlin (2001)
4. Martinetz, T., Martinetz, T., Berkovich, S., Schulten, K.: "Neural-gas" Network for Vector Quantization and its Application to Time-Series Prediction. IEEETransactions on Neural Networks 4(4), 558-569 (1993)
5. Carpinteiro, O.A.S.: A Hierarchical Self-Organizing Map Model for Sequence Recognition. In: Proc. of ICANN, vol. 2, pp. 815-820. Springer, London (1998)
6. Hammer, B., Villmann, T.: Classification using non standard metrics. In: Proc. of ESANN, pp. 303-316 (2005)
7. Euliano, N.R., Principe, J.C.: A Spatio-Temporal Memory Based on SOMs with Activity Diffusion. In: Oja (ed.) Kohonen Maps, pp. 253-266. Elsevier, Amsterdam (1999)
8. Hammer, B., Micheli, A., Sperduti, A., Strickert, M.: Recursive self-organizing network models. Neural Networks 17(8-9), 1061-1085 (2004)
9. Hammer, B., Micheli, A., Neubauer, N., Sperduti, A., Strickert, M.: Self Organizing Maps for Time Series. In: Proc. of WSOM, Paris, France, pp. 115-122 (2005)
10. Hammer, B., Micheli, A., Sperduti, A.: A general framework for unsupervised processing of structured data. In: Proc. of ESANN, vol. 10, pp. 389-394 (2002)
11. Chappell, G.J., Taylor, J.G.: The Temporal Kohonen map. Neural Networks 6(3), 441-445 (1993)
12. Koskela, T., Varsta, M., Heikkonen, J., Kaski, K.: Temporal sequence processing using Recurrent SOM. In: Proc. of KES, pp. 290-297. IEEE, Los Alamitos (1998)
13. Voegtlin, T.: Recursive Self-Organizing Maps. Neural Networks 15(8-9) (2002)
14. Hagenbuchner, M., Sperduti, A., Tsoi, A.C.: A Self-Organizing Map for adaptive processing of structured data. Neural Networks 14(3), 491-505 (2003)
15. Lambrinos, D., Scheier, C., Pfeifer, R.: Unsupervised Classification of Sensory-Motor states in a Real World Artifact using a Temporal Kohonen Map. In: Proc. of ICANN, vol. 2, EC2, pp. 467-472 (1995)
16. Farkas, I., Crocker, M.: Recurrent networks and natural language: exploiting selforganization. In: Proc. of CogSci (2006)
17. Farka, I., Crocker, M.W.: Systematicity in sentence processing with a recursive Self-Organizing Neural Network. In: Proc. of ESANN (2007)
18. Trentini, F., Hagenbuchner, M., Sperduti, A., Scarselli, F., Tsoi, A.: A Self-Organising Map approach for clustering of XML documents. In: WCCI (2006)
19. Estevez, P.A., Zilleruelo-Ramos, R., Hernandez, R., Causa, L., Held, C.M.: Sleep Spindle Detection by Using Merge Neural Gas. In: WSOM (2007)
20. Martinetz, T.: Competitive Hebbian Learning Rule Forms Perfectly Topology Preserving Maps. In: Proc. of ICANN, pp. 427-434. Springer, Heidelberg (1993)
21. Fritzke, B.: A self-organizing network that can follow non-stationary distributions. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997.
22. LNCS, vol. 1327, pp. 613-618. Springer, Heidelberg (1997)
23. Kyan, M., Guan, L.: Local variance driven self-organization for unsupervised clustering. In: Proc. of ICPR, vol. 3, pp. 421-424 (2006)
24. Fritzke, B.: Vektorbasierte Neuronale Netze. PhD thesis, Uni Erlangen (1998)
25. Strickert, M., Hammer, B., Blohm, S.: Unsupervised recursive sequence processing. Neurocomputing 63, 69-97 (2005)
26. Kamada, T., Kawai, S.: An algorithm for drawing general undirected graphs. Inf. Process. Lett. 31(1), 7-15 (1989)