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基于涌现自组织映射的聚类分析与可视化处理
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2009-07-11
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Kohonen 自组织特征映射可实现高维模式空间到低维拓扑结构的映射，借此可进行模
式聚类分析及高维数据的二维可视化。但当输入样本数目较多、复杂度较大时，采用KSOM将使相邻类簇间发生大面积重叠，降低聚类效果。本文通过利用涌现自组织特征映射神经网络对数据进行聚类分析，并通过无边界U 矩阵实现可视化功能。测试结果表明，借助ESOM模型进行数据的聚类分析与可视化在诸多方面表现出优越的性能。
关键词： 涌现自组织特征映射；聚类； U 矩阵
Abstract: Kohonen Self-Organizing Maps (KSOM) can implement a mapping from high-dimensional pattern space to low-dimensional topological structure. With the number of sampling data increasing and their complexity enhancing, the adjacent clusters of KSOM may be overlap in a common region. This can reduce the effect of data clustering and visualization. To facilitate clustering analysis and visualization of data, the Emergent Self-Organizing Feature Maps (ESOM) and a boundless U-matrix are needed. It is proved that ESOM model is feasible and effective for high-dimensional data clustering and visualization processing.
Keywords: Emergent Self-Organizing Feature Maps; Clustering; U-Matrix
式聚类分析及高维数据的二维可视化。但当输入样本数目较多、复杂度较大时，采用KSOM将使相邻类簇间发生大面积重叠，降低聚类效果。本文通过利用涌现自组织特征映射神经网络对数据进行聚类分析，并通过无边界U 矩阵实现可视化功能。测试结果表明，借助ESOM模型进行数据的聚类分析与可视化在诸多方面表现出优越的性能。
关键词： 涌现自组织特征映射；聚类； U 矩阵
Abstract: Kohonen Self-Organizing Maps (KSOM) can implement a mapping from high-dimensional pattern space to low-dimensional topological structure. With the number of sampling data increasing and their complexity enhancing, the adjacent clusters of KSOM may be overlap in a common region. This can reduce the effect of data clustering and visualization. To facilitate clustering analysis and visualization of data, the Emergent Self-Organizing Feature Maps (ESOM) and a boundless U-matrix are needed. It is proved that ESOM model is feasible and effective for high-dimensional data clustering and visualization processing.
Keywords: Emergent Self-Organizing Feature Maps; Clustering; U-Matrix
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