Aiming to some flaw, people bring forward to use Self-Organizing Feature Map on collecting data to make clustering and watching at first, and obtain principium information about some collection data.
针对这些缺点提出先利用自组织映射的方法对采集的数据进行聚类和可视化，获得一些关于采集到的数据的初步信息。
In this paper, the basic principle of the clustering algorithm based on self-organizing feature map network is discussed, and pointed out its defects.
本文讨论了基于自组织特征映射网络聚类算法的基本原理，并指出了算法的缺陷。
Self Organizing Map is a method of artificial neural network, which implements pattern recognition and data clustering simultaneously.
自组织特征映射是一种人工神经网络方法，可以同时实现模式识别和数据分类。
In this paper, we propose a model-based, self organizing feature map algorithm for the clustering of variable-length sequences.
本文提出一种基于模型的、适合变长符号序列的自组织聚类算法。
This paper tries to make some improvements on applying Self-Organizing-Map (SOM) to automatic clustering of Chinese nouns, so as to generate a better Chinese semantic map.
本文试图对自组织映射神经网络（SOM）应用于汉语名词语义自动聚类做某些改进。
This paper tries to make some improvements on applying Self-Organizing-Map (SOM) to automatic clustering of Chinese nouns, so as to generate a better Chinese semantic map.
本文试图对自组织映射神经网络（SOM）应用于汉语名词语义自动聚类做某些改进。
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