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Multiple Kernel Shadowed Clustering in Approximated Feature Space

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Data Mining and Big Data (DMBD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10943))

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Abstract

Compared to conventional fuzzy clustering, shadowed clustering possesses several advantages, such as better modeling of the uncertainty for the overlapped data, reduction of computation and more robust to outliers because of the generated shadowed partitions. Based on the construction of a set of pre-specific kernels, multiple kernel fuzzy clustering presents more flexibility in fuzzy clustering than kernel fuzzy clustering. However, it is unattainable to large dataset because of its high computational complexity. To solve this problem, a new multiple kernel shadowed clustering in approximated feature space is proposed herein, using Random Fourier Features and Spherical Random Fourier Features to approximate radial basis kernels and polynomial kernels, respectively. To optimize the kernel weight, maximum-entropy regularization is used. The results of our proposed algorithm on Iris and Letter Recognition datasets show better performance than other algorithms in comparison.

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Acknowledgment

This research is supported by university of Macau RC MYRG2015-00148-FST, Science and Technology Development Fund, Macao S.A.R (097/2015/A3) and National Nature Science Foundation of China under Grant No.: 61673405.

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Authors and Affiliations

  1. University of Macau, Taipa, Macau, China

    Yin-Ping Zhao, Long Chen & C. L. Philip Chen

Authors
  1. Yin-Ping Zhao
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  2. Long Chen
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  3. C. L. Philip Chen
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Corresponding author

Correspondence to Long Chen .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Tongji University, Shanghai, China

    Qirong Tang

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Zhao, YP., Chen, L., Chen, C.L.P. (2018). Multiple Kernel Shadowed Clustering in Approximated Feature Space. In: Tan, Y., Shi, Y., Tang, Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science(), vol 10943. Springer, Cham. https://doi.org/10.1007/978-3-319-93803-5_25

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  • DOI: https://doi.org/10.1007/978-3-319-93803-5_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93802-8

  • Online ISBN: 978-3-319-93803-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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