Dynamic self-organizing maps with controlled growth for knowledge discovery
Dynamic self-organizing maps with controlled growth for knowledge discovery
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DOI：
10.1109/72.846732
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发表时间：
2000-05-01
影响因子：
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通讯作者：
Srinivasan, B
中科院分区：
文献类型：
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作者：
Alahakoon, D;Halgamuge, SK;Srinivasan, B
生长自组织图（GSOM）作为自组织图（SOM）的扩展版本被提出，对于知识发现应用具有显着的优势。本文详细介绍了 GSOM 算法，并研究了可用于测量和控制 GSOM 扩展的扩展因子的效果。扩展因子与数据的维度无关，因此可以用作生成不同维度的地图的控制措施，然后可以更准确地进行比较和分析。扩展因子也被提出作为一种使用 GSOM 实现数据集层次聚类的方法。这种层次聚类允许数据分析师在层次结构的较高级别上识别重要且有趣的聚类，并因此继续仅对感兴趣的聚类进行更精细的聚类。因此，一开始只创建一个低扩散因子的小地图，即使是非常大的数据集也可以生成该地图，然后对数据的选定部分进行进一步分析，因此体积较小，因此，该方法甚至可以方便地分析非常大的数据集。
The growing self-organizing map (GSOM) has been presented as an extended version of the self-organizing map (SOM), which has significant advantages for knowledge discovery applications. In this paper, the GSOM algorithm is presented in detail and the effect of a spread factor, which can be used to measure and control the spread of the GSOM, is investigated. The spread factor is independent of the dimensionality of the data and as such can be used as a controlling measure for generating maps with different dimensionality, which can then be compared and analyzed with better accuracy. The spread factor is also presented as a method of achieving hierarchical clustering of a data set with the GSOM. Such hierarchical clustering allows the data analyst to identify significant and interesting clusters at a higher level of the hierarchy, and as such continue with finer clustering of only the interesting clusters. Therefore, only a small map is created in the beginning with a low spread factor, which can be generated for even a very large data set, Further analysis is conducted on selected sections of the data and as such of smaller volume, Therefore, this method facilitates the analysis of even very large data sets.