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An information-theoretic fuzzy C-spherical shells clustering algorithm

机译：信息理论的模糊C球壳聚类算法

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摘要

In this paper, we shall investigate source compression coding theorem from the perspective of robust fuzzy clustering that is derived from the basic fuzzy C-spherical shells (FCSS) algorithm. The proposed information fuzzy C-spherical shells (IFCSS) algorithm tackles the intertwined robust fuzzy clustering problems of outlier detection, prototype initialization and cluster validity in a unified framework of information clustering. The IFCSS addresses fuzzy membership and typicality issues separately through the minimum number and the sensitivity of hyper-parameters in the clustering objective function. We use the basic FCSS algorithm for the clustering phase to minimize the number of hyper-parameters and reduce the difficulty of prototype initialization, especially for spherical shells data. The robustness of IFCSS against noisy points (outliers) is obtained by the maximizing the mutual information (MI), which also provides a good criterion for prototype initialization. The clustering validity criterion for the IFCSS is proposed based on the structural risk minimization principle to achieve an optimal trade-off between the empirical risk (clustering) and model complexity control (cluster number). The effectiveness of the proposed algorithms for clustering spherical shells is supported by experimental results.
机译：在本文中，我们将从基于基本模糊C球壳（FCSS）算法的鲁棒模糊聚类的角度研究源压缩编码定理。提出的信息模糊C球壳（IFCSS）算法在统一的信息聚类框架中解决了异常检测，原型初始化和聚类有效性相互交织的鲁棒模糊聚类问题。 IFCSS通过最小数目和聚类目标函数中超参数的敏感性分别解决了模糊隶属度和典型性问题。我们在聚类阶段使用基本的FCSS算法，以最大程度地减少超参数的数量，并减少原型初始化的难度，尤其是对于球形壳数据。 IFCSS对噪声点（异常值）的鲁棒性是通过最大化互信息（MI）获得的，这也为原型初始化提供了一个很好的标准。提出了基于结构风险最小化原理的IFCSS聚类有效性准则，以在经验风险（聚类）和模型复杂度控制（聚类数）之间实现最佳折衷。实验结果证明了所提出算法对球壳聚类的有效性。

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  • 来源
    《Fuzzy sets and systems》 |2010年第13期|p.1755-1773|共19页
  • 作者

    Qing Song; Xulei Yang; Yeng Chai Soh; Zhi Min Wang;

  • 作者单位

    School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore;

    School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore;

    School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore;

    School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    robust fuzzy clustering; C-shell data set; information theory;

    机译：鲁棒的模糊聚类;C-shell数据集;信息论;

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