Common approaches to unsupervised learning include k-Means, hierarchical clustering, and self-organizing maps.
无监管学习的常见方法包括k - Means、分层集群和自组织地图。
This paper presents a new method of text clustering by using the latent semantic index (LSI) and self-organizing neural network (SNN).
根据隐含语义索引（LSI）理论和动态自组织映射神经网络理论，提出了一种文本聚类的新方法。
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。
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。
A method that applies the clustering function of SOFM (Self-Organizing Feature Maps) network is proposed for autonomous star pattern recognition.
介绍了一种利用自组织特征映射(SOFM)网络的聚类功能进行全天星图识别的方法。
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。
The effect of samples training on BP neural network performance with the clustering characteristic of self-organizing competitive network is improved.
通过自组织竞争网络的聚类特征，改善样本训练对BP网络性能的影响。
An autonomous star pattern recognition method using the tri-star clustering function of SOFM (Self-Organizing Feature Maps) network is described.
介绍了一种利用SOFM(自组织特征映射)网络的聚类功能进行全天星图识别的算法。
To facilitate clustering analysis and visualization of data, the Emergent Self-Organizing Feature Maps (ESOM) and a boundless U-matrix are needed.
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。
It implements two original algorithms specifically designed for clustering short time series together with hierarchical clustering and self-organizing maps.
它实现了两个专为短的时间序列聚类与聚类和自组织映射的原始算法。
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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