基于涌现自组织映射的聚类分析与可视化处理

Data Clustering and Visualization based on Emergent Self-Organizing Feature Maps

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。

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.