Abstract
This work introduces a new fuzzy c-regression models with various loss functions. The algorithm consists in solving a sequence of weighted quadratic minimization problems where the weights used for the next iteration depend on values of models residuals for the current iteration. Simulations on real-life ECG signals are realized to evaluate the performance of the fuzzy clustering method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abonyi, J., Feil, B., Németh, S.Z., Arva, P.: Fuzzy clustering based segmentation of time-series. In: Berthold, M., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol. 2810, pp. 275–285. Springer, Heidelberg (2003)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1982)
Davé, R.N.: Characterization and detection of noise in clustering. Pattern Recognition Letters 12(11), 657–664 (1991)
Davé, R.N., Krishnapuram, R.: Robust clustering methods: A unified view. IEEE Transactions on Fuzzy Systems 5(2), 270–293 (1997)
Dunn, J.C.: A fuzzy relative of the isodata process and its use in detecting compact well-separated cluster. Journal of Cybernetics 3(3), 32–57 (1973)
Girolami, M.: Mercer kernel-based clustering in feature space. IEEE Transactions on Neural Networks 13(3), 780–784 (2002)
Hathaway, R.J., Bezdek, J.C.: Switching regression models and fuzzy clustering. IEEE Transactions on Fuzzy Systems 1(3), 195–204 (1993)
Hathaway, R.J., Bezdek, J.C.: Generalized fuzzy c-means clustering strategies using L p norm distances. IEEE Transactions on Fuzzy Systems 8(5), 576–582 (2000)
Krishnapuram, R., Nasraoui, O., Frigui, H.: The fuzzy c-spherical shells algorithm: A new approach. IEEE Transactions on Neural Networks 3(5), 663–671 (1992)
Łęski, J.M.: Robust possibilistic clustering. Archives of Control Sciences 10(3-4), 141–155 (2000)
Łęski, J.M.: An ε-insensitive approach to fuzzy clustering. International Journal of Applied Mathematics and Computer Science 11(4), 993–1007 (2001)
Łęski, J.M.: Computationally effective algorithm to the ε-insensitive fuzzy clustering. System Science 28(3), 31–50 (2002)
Łęski, J.M.: ε-insensitive fuzzy c-regression models: Introduction to ε-insensitive fuzzy modeling. IEEE Transactions Systems, Man and Cybernetics - Part B: Cybernetics 34(1), 4–15 (2004)
Łęski, J.M., Henzel, N.: Generalized ordered linear regression with regularization. Bulletin of the Polish Academy of Sciences: Technical Sciences 60(3), 481–489 (2012)
Łęski, J.M., Owczarek, A.J.: A time-domain-constrained fuzzy clustering method and its application to signal analysis. Fuzzy Sets and Systems 155(2), 165–190 (2005)
Pedrycz, W.: Conditional fuzzy c-means. Pattern Recognition Letters 17(6), 625–631 (1996)
Pedrycz, W.: Distributed collaborative knowledge elicitation. Computer Assisted Mechanics and Engineering Sciences 9(1), 87–104 (2002)
Pedrycz, W., Waletzky, J.: Fuzzy clustering with partial supervision. IEEE Transactions Systems, Man and Cybernetics - Part B: Cybernetics 27(5), 787–795 (1997)
Policker, S., Geva, A.B.: Nonstationary time series analysis by temporal clustering. IEEE Transactions Systems, Man and Cybernetics - Part B: Cybernetics 30(2), 339–343 (2000)
Ruspini, E.H.: A new approach to clustering. Information and Control 15(1), 22–32 (1969)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Momot, A., Momot, M., Leski, J.M. (2014). An Application of Fuzzy C-Regression Models to Characteristic Point Detection in Biomedical Signals. In: Gruca, D., Czachórski, T., Kozielski, S. (eds) Man-Machine Interactions 3. Advances in Intelligent Systems and Computing, vol 242. Springer, Cham. https://doi.org/10.1007/978-3-319-02309-0_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-02309-0_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02308-3
Online ISBN: 978-3-319-02309-0
eBook Packages: EngineeringEngineering (R0)