Polynomial approximation schemes and exact algorithms for optimum curve segmentation problems

Discrete Applied Mathematics

Volumn 144, Issue 1-2, 2004, Pages 158-172

  Minoux, M ^a  

^a SORBONNE UNIVERSITÉ   (France)

Author keywords

Continuous segmentation; Discrete segmentation; Polynomial approximation

Indexed keywords

DECOMPOSITION; GRAPH THEORY; LINEAR PROGRAMMING; MATRIX ALGEBRA; POLYNOMIAL APPROXIMATION; PROBLEM SOLVING;

BINARY VARIABLES; CONTINUOUS SEGMENTATION; DISCRETE SEGMENTATION;

ALGORITHMS;

EID: 4544331332     PISSN: 0166218X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dam.2004.05.003     Document Type: Article

References (13)

1. R.K. Ahuja, T.L. Magnanti, J.B. Orlin, Network Flows: Theory, Algorithms and Applications, Prentice-Hall, Englewood Cliffs, NJ, 1993.
2. R. Bellman, Dynamic Programming, Princeton University Press, Princeton, NJ, 1957.
3. K.S. Booth, G.S. Lueker, Testing for the consecutive ones property, interval graphs and graph planarity using P-Q-tree algorithms, J. Comput. System Sci. 13 (1976) 335-379.
4. W.D. Fisher, On grouping for maximum homogeneity, J. Amer. Statist. Assoc. 53 (1958) 789-798.
5. D.R. Fulkerson, D.A. Gross, Incidence matrices and interval graphs, Pacific J. Math. 15 (1965) 833-835.
6. M. Gondran, M. Minoux, Graphs and Algorithms, Wiley, New York, 1984.
7. G. Hebrail, B. Hugueney, Symbolic representation of long time series, Fourth European Conference on Principles and Practice of Knowledge Discovery in Databases, 2000.
8. B. Huguenay, B. Bouchon-Meunier, Time-series segmentation and symbolic representation: from process-monitoring to data mining, B. Reusch(Ed.), Computational Intelligence, Theory and Applications, Lecture Notes in Computer Sciences, Vol. 2206, Springer, Berlin, 2001, pp. 118-123.
9. E. Keogh, M.J. Pazanni, An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback, in: D. Heckerman, H. Mannila, D. Pregibon, R. Uthurusamy (Eds.), Proceedings of International Conference on Knowledge Discovery and Data Mining, AAAI Press, 1998.
10. E. Keogh, S. Chu, D. Hart, M. Pazanni, An online algorithm for segmenting time series, IEEE Conference on Data Mining, 2001.
11. P.L. Love, M. Simaan, Automatic recognition of primitive changes in manufacturing process signals, Pattern Recognition 21 (4) (1998) 333-342.
12. G.L. Nemhauser, L.A. Wolsey, Integer and Combinatorial Optimization, Wiley, New York, 1998.
13. C.H. Papadimitriou, K. Steiglitz, Combinatorial Optimization - Algorithms and Complexity, Prentice-Hall, Englewood Cliffs, NJ, 1982.