Kohonen 自組織特征映射可實現高維模式空間到低維拓撲結構的映射，借此可進行模
式聚類分析及高維數據的二維可視化。但當輸入樣本數目較多、復雜度較大時，采用KSOM將使相鄰類簇間發生大面積重疊，降低聚類效果。本文通過利用涌現自組織特征映射神經網絡對數據進行聚類分析，并通過無邊界U 矩陣實現可視化功能。測試結果表明，借助ESOM模型進行數據的聚類分析與可視化在諸多方面表現出優越的性能。
關鍵詞： 涌現自組織特征映射；聚類； U 矩陣
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