Temporal self-organizing maps for telecommunications market segmentation

Neurocomputing

Volumn 71, Issue 13-15, 2008, Pages 2880-2892

  D'Urso, Pierpaolo ^a   De Giovanni, Livia ^b  

^a UNIVERSITY OF MOLISE   (Italy)

^b LUMSA UNIVERSITY   (Italy)

Author keywords

Dynamic tandem analysis; K means clustering; Kohonen SOM networks; Multivariate time sequences; Telecommunications operators

Indexed keywords

CLUSTER ANALYSIS; COMMERCE; CONFORMAL MAPPING;

DYNAMIC TANDEM ANALYSIS; K-MEANS CLUSTERING; KOHONEN SOM NETWORKS; MULTIVARIATE TIME SEQUENCES; TELECOMMUNICATIONS OPERATORS;

SELF ORGANIZING MAPS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CLUSTER ANALYSIS; DATA ANALYSIS; FACTORIAL ANALYSIS; INFORMATION PROCESSING; MULTIVARIATE ANALYSIS; PRIORITY JOURNAL; TELECOMMUNICATION;

EID: 56449090384     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.07.012     Document Type: Article

References (34)

1. G.A. Barreto, A.F.R. Araújo, S. Kremer, A taxonomy for spatiotemporal connectionist networks revisited: The unsupervised case, Neural Comput. 15 (6) (2003) 1255-1320.
2. H.U. Bauer, M. Hermann, T. Villmann, Neural maps and topographic vector quantization, Neural Networks 12 (4-5) (1999) 659-676.
3. Z. Bohte, D. Cepar, K. Košmelj, Clustering of Time Series, COMPSTAT'80, Physica-Verlag, Heidelberg, 1980, pp. 587-593.
4. M. Budinich, J.G. Taylor, On the ordering conditions for self-organising maps, Neural Comput. 7 (2) (1995) 284-289.
5. A. Carlier, Factor Analysis of Evolution and Cluster Methods on Trajectories, COMPSTAT'86, Physica-Verlag, Heidelberg, Wien, 1986, pp. 140-145.
6. P.L. Conti, L. De Giovanni, On the mathematical treatment of self organization: Extension of some classical results, Int. Conf. Artif. Neural Networks (1991) 1089-1812.
7. R. Coppi, P. D'Urso, The geometric approach to the comparison of multivariate time trajectories, in: S. Borra, R. Rocci, M. Vichi, M. Schader (Eds.), Advances in Data Science and Classification, Springer, Heidelberg, 2001, pp. 93-100.
8. R. Coppi, P. D'Urso, The dual dynamic factor analysis model, in: W. Gaul, G. Ritter (Eds.), Classification, Automation, and New Media, Springer, Heidelberg, 2002, pp. 47-58.
9. R. Coppi, F. Zannella, L'analisi fattoriale di una serie temporale multipla relativa allo stesso insieme di unità statistiche, in: Proceedings of the XXIX Scientific Meeting of the Italian Statistical Society (SIS), Bologna, Italy, 1978, pp. 61-77.
10. I. Corazziari, Dynamic factor analysis, in: M. Vichi, O. Opitz (Eds.), Classification and Data Analysis. Theory and Application, Springer, Heidelberg, 1999, pp. 171-178.
11. P. D'Urso, Dissimilarity measures for time trajectories, J. Ital. Stat. Soc. 1-3 (2000) 53-83.
12. P. D'Urso, Fuzzy clustering for data time array with inlier and outlier time trajectories, IEEE Trans. Fuzzy Systems 13 (5) (2005) 583-604.
13. J.C. Fort, SOM's mathematics, Neural Networks 19 (6-7) (2006) 812-816.
14. M. Hagenbuchner, A. Sperduti, A.C. Tsoi, A self-organizing map for adaptive processing of structured data, IEEE Trans. Neural Networks 14 (3) (2003) 491-505.
15. G.-B. Huang, H.A. Babri, H.-T. Li, Ordering of self-organizing maps in multi-dimensional cases, Neural Comput. 10 (1998) 19-23.
16. Y. Kakizawa, H. Shumway, M. Taniguchi, Discriminant and clustering for multivariate time series, J. Am. Stat. Assoc. 93 (441) (1998) 328-340.
17. T. Kohonen, Self-Organization and Associative Memory, Springer, Berlin, 1989.
18. T. Kohonen, Self-Organizing Maps, Springer, Berlin, 1997.
19. K. Košmelj, A two-step procedure for clustering time varying data, J. Math. Soc. 12 (3) (1986) 315-326.
20. K. Košmelj, B. Lachet, Aspect temporel des variables hydriques du haut rhône français, Rev. Stat. Appl. XXXI (2) (1983) 5-18.
21. T.W. Liao, Clustering of time series data - a survey, Pattern Recogn. 38 (11) (2005) 1857-1874.
22. E.A. Maharaj, A significance test for classifying ARMA models, J. Stat. Comput. Simul. 54 (1996) 305-331.
23. T.M. Martinetz, S.G. Berkovich, K.J. Schulten, 'Neural-gas' network for vector quantization and its application to timeseries prediction, IEEE Trans. Neural Networks 4 (4) (1993) 558-569.
24. D. Piccolo, A distance measure for classifying ARIMA models, J. Time Series Anal. 11 (2) (1990) 153-164.
25. A. Raveh, A graphic display for characterization of seasonal pattern similarities of time series, Statistician 30 (3) (1981) 179-192.
26. A. Raveh, C.S. Tapiero, Finding common seasonal patterns among time series, J. Econ. 12 (1980) 353-363.
27. H. Ritter, K. Schulten, On the stationary state of Kohonen's self-organizing sensory mapping, Biol. Cybern. 54 (1986) 99-106.
28. M. Strickert, B. Hammer, Neural gas for sequences, in: T. Yamakawa (Ed.), Proceedings of the Workshop on Self-Organizing Networks (WSOM), Kyushu Institute of Technology, 2003, pp. 53-58.
29. M. Strickert, B. Hammer, S. Blohm, Unsupervised recursive sequences processing, Neurocomputing 63 (2005) 69-98.
30. A. Ultsch, Data mining and knowledge discovery with emergent self-organizing feature maps for multivariate time series, in: E. Oja, S. Kaski (Eds.), Kohonen Maps, 1999, pp. 33-46.
31. M. Varsta, J. Heikkonen, J. Lampinen, J.D.R. Millán, Temporal Kohonen map and the recurrent self-organizing map: Analytical and experimental comparison, Neural Process. Lett. 13 (3) (2001) 237-251.
32. T. Villmann, J.C. Claussen, Magnification control in self-organizing maps and neural gas, Neural Computation 18 (2) (2006) 446-469.
33. J.C. Wiemer, The time-organized map algorithm: Extending the self-organizing map to spatiotemporal signals, Neural Comput. 15 (5) (2003) 1143-1171.
34. A. Wismuller, O. Lange, D.R. Dersch, G.L. Leinsinger, K. Hahn, B. Putz, D. Auer, Cluster analysis of biomedical image time series, Int. J. Comput. Vis. 46 (2) (2002) 103-128.