In this paper, the self-clustering and evolving processes and quantity of clustering for complex systems are investigated.
研究复杂系统的自聚集演化过程和聚集量。
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 proposes a novel Weight Clustering Algorithm for mobile self-organized network.
本文提出了一种新型的适用于移动自组织网络的权值分群算法。
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。
Since an FCM based self adaptive fuzzy clustering technique is employed to determine the proper structure of the FNN and set the initial weights in advance, the network can be trained rapidly.
由于预先运用基于FCM的自适应模糊聚类方法确定模糊神经元网络合理的结构，并设置网络的初始权值，从而可提高网络的训练速度。
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。
This clustering algorithm can on-line partition the input data, pointwise update the clusters, and self-organize the fuzzy neural structure.
此聚类算法可以在线地划分输入数据，逐点地更新聚类，自己组织模糊神经网络的结构。
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。
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 order to prolong the lifetime of the WSN, it is essential to design a self-organized clustering algorithm to accommodate the characteristic of sensor network.
为了有效延长WSN网络的生存时间，需要设计能量有效的自组织成簇机制，以适应无线传感器网络的特点。
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。
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。
This paper proposed an improved routing update algorithm - a dynamically self-adaptive routing update algorithm based on clustering mechanism.
提出了一种改进的路由更新算法—基于分簇机制的动态自适应路由更新算法。
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。
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。
The effectiveness of the method in textile clustering and grading is proved by using 43 cotton samples which are self-knitted by Ecole Nationale Supérieure des Arts et Industries Textiles, France.
通过对法国鲁贝高等纺织工程学院自织的43块棉针织物的分析，证明了以上方法在处理纺织品质量分类、分级问题中的有效性。
And based on the experimental results of multi-dimensional data clustering, anomaly detection matrix is determined through identifying the training sample set and the machine self-learning.
然后根据对多维数据聚类的实验分析结果，通过对样本集的训练进行标识和机器自学习过程来判别异常检测矩阵。
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。
Most clustering algorithms can not meet the demand of speed and self-adapting about text clustering.
目前多数聚类算法不能很好地适应文本聚类的快速自适应需求。
A quantum self-organization feature mapping networks model and its clustering algorithm are presented.
提出一种量子自组织特征映射网络模型及聚类算法。
The experiments indicate that the rough K-means based on self-adaptive weights is an effective rough clustering algorithm.
实验结果表明，基于自适应权重的粗糙K均值算法是一种较优的聚类算法。
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。
On the basis of the above research, a self-adaptive clustering routing algorithm sensitive to position is proposed.
在上述研究的基础上，提出了一种对位置敏感的自适应分簇路由算法。
Analyze the parameters of the fuzzy clustering method, ensure the self-adaptive clustering of the generators by the fuzzy statistical index.
对模糊聚类的方法进行了参数分析，引入指标保证了发电机分群的自适应性。
Furthermore, the self-adaptive weights arc obtained from the Gaussian distance ration in cluster approximation, which can lead to the more accurate clustering results.
此外，该算法采用近似集合中的高斯距离比例来表现样本权重，从而可以在多种数据分布上得到更精确的聚类结果。
Furthermore, the self-adaptive weights arc obtained from the Gaussian distance ration in cluster approximation, which can lead to the more accurate clustering results.
此外，该算法采用近似集合中的高斯距离比例来表现样本权重，从而可以在多种数据分布上得到更精确的聚类结果。
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