TGSOM:一种用于数据聚类的动态自组织映射神经网络
Tgsom: a new dynamic self-organizing maps for data clustering
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摘要: 针对传统Kohonen自组织特征映射(SOFM)神经网络模型结构需预先指定的限制,提出一种新的树形动态自组织映射(TGSOM)神经网络,当用于数据挖掘时该网络以其生成速度快可视性好具有显著优越性。该文详尽描述了该网络模型的生成算法,研究了算法中扩展因子的作用。扩展因子与训练样本数据的维数无关,其作用是控制网络的生长,扩展因子可以反映数据聚类的精度,即扩展因子值的大小与聚类精度的高低成正比。在聚类的不同阶段使用大小不等的扩展因子还可以实现层次聚类。Abstract: A Tree-structured Growing Self-Organizing Maps (TGSOM) is presented as an extended version of the Self-Organizing Feature Maps (SOFM), which has significant advantages for data mining applications. The TGSOM algorithm is presented in detail and the effect of a spread factor, which can be used to measure and control the spread of the TGSOM, 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 TGSOM. 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.
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