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.
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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).
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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
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DOI: https://doi.org/10.1007/978-981-13-1648-7_13
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