Abstract
We have proposed multi-objective Gene Expression Programming (GEP) based automatic clustering method (do not need prior knowledge), which is denoted as MOGEPC. In our algorithm, we adopt GEP based multi-objective optimization, which has a powerful global search ability to optimize the two objective functions, namely, compactness and connectedness simultaneously. We use center-based encoding to generate chromosomal in the encoding phase and expression tree (ET) to decode chromosome to cluster centers. The introduction of multi-objective in GEP is helpful to make the clustering quality of data sets with different structures better. Finally, we apply MOGEPC on artificial data sets, real data sets from UCI. Experiments show that MOGEPC is robust in clustering various data sets without any apriori information.
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
Similar content being viewed by others
References
Han, J.W., Kambr, M.: Data Mining Concepts and Techniques. Higher Education Press, Beijing (2001)
Lee, J., Lee, D.: Dynamic characterization of cluster structures for robust and inductive support vector clustering. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1869–1874 (2006)
McQueen, J.: Some methods for classification and analysis of multivariate observations. In: 5th Berkeley Symposium in Mathematics, Statistics and Probability, University of California Press, vol. 1, no. 14, pp. 281–297 (1967)
Handl, J., Knowles, J.: An evolutionary approach to multi-objective clustering. IEEE Trans. Evol. Comput. 11(1), 56–76 (2007)
Wikaisuksakul, S.: A multi-objective genetic algorithm with fuzzy c-means for automatic data clustering. Appl. Soft Comput. 24, 679–691 (2014)
Chen, E., Wang, F.: Dynamic clustering using multi-objective evolutionary algorithm. In: Hao, Y., et al. (eds.) CIS 2005. LNCS (LNAI), vol. 3801, pp. 73–80. Springer, Heidelberg (2005). https://doi.org/10.1007/11596448_10
Nanda, S.J., Panda, G.: Automatic clustering algorithm based on multi-objective immunized PSO to classify actions of 3D human models. Eng. Appl. Artif. Intell. 26(5), 1429–1441 (2013)
Liu, R., Lei, Q., Liu, J., Jiao, L.: A population diversity-oriented gene expression programming for function finding. In: Deb, K., et al. (eds.) SEAL 2010. LNCS, vol. 6457, pp. 215–219. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17298-4_22
Ferreira, C.: Function finding and the creation of numerical constants in gene expression programming. In: Benítez, J.M., Cordón, O., Hoffmann, F., Roy, R. (eds.) Advances in Soft Computing, pp. 257–265. Springer, London (2003). https://doi.org/10.1007/978-1-4471-3744-3_25
Chen, Y., Tang, C., Zhu, J., Li, C., Qiao, S., Li, R.: Clustering without prior knowledge based on gene expression programming. In: International Conference on Natural Computation, vol. 3, pp. 451–455 (2007)
Ni, Y., Du, X., Xie, D., Ye, P., Zhang, K.: A multi-objective cluster algorithm based on GEP. In: IEEE International Conference on Cloud Computing and Big Data (CCBD 2014), pp. 33–38 (2014)
Ferreira, C.: Gene expression programming: a new adaptive algorithm for solving problem. Complex Syst. 13(2), 87–129 (2001)
Deb, K., Pratap, A., Agarwal, S., Meyrivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Deb, K.: Multi-objective evolutionary algorithms: introducing bias among pareto-optimal solutions. In: Ghosh, A., Tsutsui, S. (eds.) Advances in Evolutionary Computing, pp. 263–292. Springer, London (2003). https://doi.org/10.1007/978-3-642-18965-4_10
Ferreira, C.: Gene Expression Programming, 2nd edn, pp. 263–292. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-32849-1
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing, 2nd edn. Springer, Heidelberg (2007)
Coelho, A.L.V., Fernandes, E., Faceli, K.: Inducing multi-objective clustering ensembles with genetic programming. Neurocomputing 74(1–3), 494–498 (2010)
Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a data set via the gap statistic. J. Roy. Stat. Soc. 63(2), 411–423 (2001)
Tasdemir, K., Merényi, E.: A validity index for prototype-based clustering of data sets with complex cluster structures. IEEE Trans. Syst. Man Cybern. Soc. 41(4), 1039–1053 (2011)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 60803098, No. 60872135), Research Fund for the Doctoral Program of Higher Education of China (No. 20070701022); the Provincial Natural Science Foundation of Shaanxi of China (2010JM8030, No. 2010JQ8023), and the Fundamental Research Funds for the Central Universities (No. K50511020014, No. K50510020011).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, R., Li, J., He, M. (2018). Multi-objective Gene Expression Programming Based Automatic Clustering Method. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-13-1648-7_13
Download citation
DOI: https://doi.org/10.1007/978-981-13-1648-7_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1647-0
Online ISBN: 978-981-13-1648-7
eBook Packages: Computer ScienceComputer Science (R0)