Abstract
In this paper, we propose multiple kernels-based interval type-2 possibilistic c-Means (MKIT2PCM) by using the kernel approach to possibilistic clustering. Kernel-based fuzzy clustering has exhibited quality of clustering results in comparison with “routine” fuzzy clustering algorithms like fuzzy c-Means (FCM) or possibilistic c-Means (PCM) not only noisy data sets but also overlapping between prototypes. Gaussian kernels are suitable for these cases. Interval type-2 fuzzy sets have shown the advantages in handling uncertainty. In this study, multiple kernel method are combined into interval type-2 possibilistic c-Means (IT2PCM) to produce a variant of IT2PCM, called multiple kernels interval type-2 possibilistic c-Means (MKIT2PCM). Experiments on various data-sets with validity indexes show the performance of the proposed algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bezdek, J.C., Ehrlich, R., Full, W.: The fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)
Das, S.: Pattern recognition using the fuzzy c-means technique. Int. J. Energy Inf. Commun 4(1), February (2013)
Patil, A., Lalitha, Y.S.: Classification of crops using FCM segmentation and texture, color feature. IJARCCE 1(6), (2012)
Krishnapuram, R., Keller, J.: A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst. 1, 98–110 (1993)
Kanzawa, Y.: Sequential cluster extraction using power-regularized possibilistic c-means. JACIII 19(1),67–73 (2015)
Krishnapuram, R., Keller, J.M.: The possibilistic c-means: insights and recommendations. IEEE Trans. Fuzzy Syst. 4, 385–393 (1996)
Sanchez, M.A., Castillo, O., Castro, J.R., Melin, P.: Fuzzy granular gravitational clustering algorithm for multivariate data. Inf. Sci. 279, 498–511 (2014)
Rubio, E., Castillo, O.: Designing type-2 fuzzy systems using the interval type-2 fuzzy c-means algorithm. Recent Adv. Hybrid Approaches Des. Intell. Syst., 37–50 (2014)
Nguyen, D.D., Ngo, L.T., Pham, L.T., Pedrycz, W.: Towards hybrid clustering approach to data classification: multiple kernels based-interval type-2 fuzzy C-means algorithms. J. Fuzzy Sets Syst. 279, 17–39 (2015)
Zhao, B., Kwok, J., Zhang, C.: Multiple kernel clustering. In: Proceedings of 9th SIAM International Conference Data Mining, pp. 638–649, 2009
Filippone, M., Masulli, F., Rovetta, S.: Applying the possibilistic C-means algorithm in Kernel-induced spaces. IEEE Trans. Fuzzy Syst. 18(3), 572–584 (2010)
Rubio, E., Castillo, O.: Interval type-2 fuzzy clustering for membership function generation. HIMA, 13–18, (2013)
Raza, M.A., Rhee, F.C.H.: Interval type-2 approach to Kernel possibilistic C-means clustering. In: IEEE International Conference on Fuzzy Systems, 2012
Chuang, K.S., Tzeng, H.L., Chen, S., Wua, J., Chen, T.-J.: Fuzzy c-means clustering with spatial information for image segmentation. Comput. Med. Imaging Graph. 30(1), 9–15 (2006)
Wang, W., Zhang, Y.: On fuzzy cluster validity indices. Fuzzy Sets Syst. 158, 2095–2117 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Vu, M.N., Ngo, L.T. (2016). A Multiple Kernels Interval Type-2 Possibilistic C-Means. In: Król, D., Madeyski, L., Nguyen, N. (eds) Recent Developments in Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 642. Springer, Cham. https://doi.org/10.1007/978-3-319-31277-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-31277-4_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31276-7
Online ISBN: 978-3-319-31277-4
eBook Packages: EngineeringEngineering (R0)