Abstract
Although the kernel-based fuzzy c-means (KFCM) algorithm utilizing a kernel-based distance measure between patterns and cluster prototypes outperforms the standard fuzzy c-means clustering for some complex distributed data, it is quite sensitive to selected kernel parameters. In this paper, we propose the stretched kernel-based fuzzy clustering method with optimized kernel parameter. The kernel parameters are updated in accordance with the gradient method to further optimize the objective function during each iteration process. To solve the local minima problem of the objective function, a function stretching technique is applied to detect the global minimum. Experiments on both synthetic and real-world datasets show that the stretched KFCM algorithm with optimized kernel parameters has better performance than other algorithms.
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Acknowledgements
This work is supported by the PAPD of Jiangsu Higher Education Institutions, the Fundamental Research Funds for the Central Universities (JUDCF12027 JUSRP211A37) and the Fund of the State Key Laboratory of ASIC and System in Fudan University (11KF003).
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Lu, C., Zhu, Z., Gu, X. (2013). Kernel Parameter Optimization in Stretched Kernel-Based Fuzzy Clustering. In: Zhou, ZH., Schwenker, F. (eds) Partially Supervised Learning. PSL 2013. Lecture Notes in Computer Science(), vol 8183. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40705-5_5
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DOI: https://doi.org/10.1007/978-3-642-40705-5_5
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