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首页> 外文期刊>International Journal of Uncertainty, Fuzziness, and Knowledge-based Systems >KERNEL METHODS FOR CLUSTERING: COMPETITIVE LEARNING AND c-MEANS
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KERNEL METHODS FOR CLUSTERING: COMPETITIVE LEARNING AND c-MEANS

机译：聚类的核心方法：竞争性学习和c均值

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摘要

Recently kernel methods in support vector machines have widely been used in machine learning algorithms to obtain nonlinear models. Clustering is an unsupervised learning method which divides whole data set into subgroups, and popular clustering algorithms such as c-means are employing kernel methods. Other kernel-based clustering algorithms have been inspired from kernel c-means. However, the formulation of kernel c-means has a high computational complexity. This paper gives an alternative formulation of kernel-based clustering algorithms derived from competitive learning clustering. This new formulation obviously uses sequential updating or on-line learning to avoid high computational complexity. We apply kernel methods to related algorithms: learning vector quantization and self-organizing map. We moreover consider kernel methods for sequential c-means and its fuzzy version by the proposed formulation.
机译：最近，支持向量机中的内核方法已广泛用于机器学习算法中以获得非线性模型。聚类是一种无监督的学习方法，它将整个数据集划分为子组，而流行的聚类算法（例如c-means）则采用内核方法。其他基于内核的聚类算法也受到了内核c均值的启发。但是，内核c均值的公式化具有很高的计算复杂度。本文给出了从竞争性学习聚类中得出的基于内核的聚类算法的另一种表示形式。显然，这种新公式使用顺序更新或在线学习来避免高计算复杂性。我们将内核方法应用于相关算法：学习矢量量化和自组织映射。此外，我们通过提出的公式考虑了用于连续c均值的核方法及其模糊版本。

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