Abstract
In this paper, we propose a novel clustering algorithm named KECA based on kernel function and evolutionary optimization. As we know, Euclidean distance based similarity metrics can help clustering algorithms handle datasets with compact super-sphere distributions perfectly, but it is undesirable for the complex structural or irregular shaped datesets. Proper mapping function can map the data in original space to high-dimensional feature space, which exposes more features and sheds light on complex structural datasets. However, clustering in feature space is time-consuming and often suffers from curse of dimensionality. Fortunately, we can cluster the mapped data in feature space which performs nonlinearly in original space with the help of kernel function in our proposed KECA. What’s more, evolutionary algorithm is used in KECA to avoid local optimal. Experimental results on artificial as well as UCI datasets show the effectiveness and robustness of the proposed KECA in compare with the genetic algorithm-based clustering and the K-means clustering.
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
Notes
References
Blake, C., Merz, C.: UCI repository of machine learning databases, Department of Information and Computer Sciences, University of California, Irvine, USA (1998)
Chen, H., Zhang, Y., Gutman, I.: A kernel-based clustering method for gene selection with gene expression data. J. Biomed. Inform. 62, 12–20 (2016)
Ding, Y., Fu, X.: Kernel-based fuzzy \(c\)-means clustering algorithm based on genetic algorithm. Neurocomputing 188, 233–238 (2015)
Dubes, R., Jain, A.K.: Clustering techniques: the user’s dilemma. Pattern Recogn. 8(4), 247–260 (1976)
Geng, X., Zhan, D.C., Zhou, Z.H.: Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 35(6), 1098–1107 (2005)
Girolami, M.: Mercer kernel-based clustering in feature space. IEEE Trans. Neural Netw. 13(3), 780–784 (2002)
Gong, M., Jiao, L., Wang, L., Bo, L.: Density-sensitive evolutionary clustering. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 507–514. Springer, Heidelberg (2007). doi:10.1007/978-3-540-71701-0_52
Kim, D.W., Lee, K.Y., Lee, D., Lee, K.H.: A kernel-based subtractive clustering method. Pattern Recogn. Lett. 26(7), 879–891 (2005)
Maulik, U., Bandyopadhyay, S.: Genetic algorithm-based clustering technique. Pattern Recogn. 33(9), 1455–1465 (2000)
Müller, K.R., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B.: An introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw. 12(2), 181–201 (2001)
Saunders, C., Stitson, M.O., Weston, J., Bottou, L., Schölkopf, B., Smola, A.J.: Support vector machine reference manual, Royal Holloway University, London, Technical report CSD-TR-98-03 (1998)
Wang, C.D., Lai, J.H.: Nonlinear clustering: methods and applications. In: Celebi, M.E., Aydin, K. (eds.) Unsupervised Learning Algorithms, pp. 253–302. Springer International Publishing, Heidelberg (2016)
Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant no. 61422209), the National Program for Support of Top-notch Young Professionals of China and the Specialized Research Fund for the Doctoral Program of Higher Education (Grant no. 20130203110011).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jiang, X., Ma, J., Lei, C. (2016). Kernel Evolutionary Algorithm for Clustering. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_1
Download citation
DOI: https://doi.org/10.1007/978-981-10-3614-9_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3613-2
Online ISBN: 978-981-10-3614-9
eBook Packages: Computer ScienceComputer Science (R0)