SOM网络在高校教师职称评审中的应用

自组织映射(SOM)是一种竞争型无指导学习的神经网络方法。SOM神经网络已广泛地应用于模式聚类、模式识别、拓扑不变性映射等方面。论文利用SOM对某高校2012年晋级副教授的46位教师的实际数据进行聚类分析,建立职称评审决策模型。首先,选取影响指标:SCI/EI篇数,一级核心论文篇数,二级核心论文篇数等作为SOM神经网络的输入模式;然后,用SOM进行聚类;最后,对聚类结果进行分析得出各类的特征和等级。实验结果表明,利用SOM对高校教师职称数据进行聚类分析是可行的、有效的,可以避开人的主观因素,更迅速客观地得到聚类结果。它为高校教师职称的评审提供了一种新的参考依据,具有较好的应用前景。     

基于SOM神经网和K-均值算法的图像分割

提出了一种基于SOM神经网络和K-均值的图像分割算法。SOM网络将多维数据映射到低维规则网格中,可以有效地用于大型数据的挖掘;而K-均值是一种动态聚类算法,适用于中小型数据的聚类。文中算法利用SOM网络将具有相似特征的象素S点映射到一个2-D神经网上,再根据神经元间的相似性,利用K-均值算法将神经元聚类。文中将该算法用于彩色图像的分割,并给出了经SOM神经网初聚类后,不同K值下神经元聚类对图像分割的结果及与单纯K-均值分割图像进行对比。     

基于免疫网络和SOM的文本聚类算法研究

文本聚类的核心问题是找到一种优化的聚类算法对文本向量进行聚类,是典型的高维数据聚类,提出一种基于自组织神经网络SOM和人工免疫网络aiNet的两阶段文本聚类算法TCBSA。新算法先用SOM神经网络进行聚类,把高维的文本数据映射到二维的平面上,然后再用aiNet对文本聚类。该方法利用SOM神经网络对高维数据降维的优点,克服了人工免疫网络对高维数据的聚类能力差的缺点。仿真实验结果表明该文本聚类算法不仅是可行的,而且具有一定的自适应能力和较好的聚类效果。     

Web文本聚类算法WTCA的研究与实现

提出了一种新的Web文本聚类算法WTCA——基于自组织特征映射神经网络(SOM)的聚类算法。该算法分为训练SOM网络及聚类分析两个阶段,具有自稳定性,无须外界给出评价函数;能够识别概念空间中最有意义的特征,抗噪音能力强。该算法应用到现代远程教育网,可以对各类远程教育站点上收集的文本资料信息自动进行聚类分析;从海量Web文本信息源中快速有效地获取重要的知识。     

基于SOM的电子商务中交易数据库二次聚类算法

研究了一种基于自组织映射（Self-Organizing Map,SOM）神经网络的交易数据库聚类方法,该方法首先对数据库中的数据项进行SOM训练学习产生初步的聚类结果,然后对第一次获得的聚类结果进行二次聚类,与直接聚类方．岳相比,该方法提高了聚类的效率,减少了计算时间。     

基于SOM的入侵检测方法研究

针对现有入侵检测系统识别率低、误报率高的问题,将SOM神经网络应用到入侵检测系统。自组织特征映射神经网络SOM(Self Organizing Feature Maps)作为一种优良的聚类工具,具有无需监督,能自动对输入模式进行聚类的优点。为验证检测方法的有效性,采用KDDCup99的训练集与测试集进行实验。     

基于自组织特征映射的聚类集成算法

为改善单一聚类算法的聚类性能,提出一种基于自组织特征映射(SOM)的聚类集成算法.该算法利用多个具有差异性的聚类成员,将原始数据集转换成一个新的特征空间矩阵;然后计算各个聚类成员的聚类综合质量,并将其作为新特征空间矩阵的属性权重,最后利用SOM神经网络进行集成,产生最终的共识聚类结果.实验结果表明,与集成前的基聚类算法和其它聚类集成算法相比,该算法能够有效地提高聚类质量.     

多获胜节点SOM及其在股票分析中的应用

为了增强自组织映射(self-organizing map,SOM)网络的动态竞争和聚类能力,提高解的精度,在无监督的SOM神经网络的基础上,通过拓广获胜节点的数量,改进网络中的邻域函数和连接权函数等方法,提出具有多获胜节点的SOM模型.为了避免多个输入样本映射到同一个输出节点,还提出了禁忌映射的方法.为了验证所提出的方法的有效性,以股票的聚类分析为实例,对该方法进行了检验.通过对每股收益、每股净资产、净资产收益率、每股经营性现金流量及净利润等5项反映上市公司综合盈利能力的财务指标进行了模拟实验,所得的数值结果表明,在标准SOM及所提出的几种多获胜节点SOM网络模型中,具有双获胜节点(SOM with 2 winners,SOM2W)的网络模型获得了最好的聚类效果.结合实验结果对网络模型的进一步分析也表明,SOM2W的聚类能力优于标准SOM及其他网络模型.该模型为股票的分析和选择提供了一种可行的途径,在金融领域具有潜在的应用价值.     

数据挖掘中聚类算法研究

聚类分析是数据挖掘领域中一个非常热门的研究课题,应用于各个领域的聚类算法非常多。本文介绍了衡量聚类算法性能的几个指标,对聚类分析进行了分类,列举了每类中典型的聚类算法,重点分析了神经网络中的自组织特征映射(SOM)算法。最后提及了聚类分析方法的应用范围以及今后需要解决的问题和发展方向。     

一种基于SOM和PAM的聚类算法

提出了一种基于自组织映射（SOM）算法和围绕中心点的划分（PAM）算法相结合的SOM-PAM聚类算法。该算法首先利用SOM算法对数据集进行“粗聚类”并得到簇数k；然后，根据簇数k再利用PAM算法对“粗聚类”结果进行聚类并得到最终聚类结果。通过实验表明，SOM-PAM算法具有比SOM算法更高的聚类效率和更好的聚类质量。     

自组织映射聚类算法在电信客户细分中的应用①

将自组织映射SOM（Self Organization Map）聚类算法应用于电信客户细分,并与采用K-means聚类算法得到的结果进行比较。实验表明,SOM可以有效的进行电信客户细分且聚类效果较优,但需付出训练时间的代价。同时对两种算法的复杂度、误差等进行了分析。     

Self-organizing map network as an interactive clustering tool — An application to group technology

The Self-Organizing Map (SOM) network, a variation of neural computing networks, is a categorization network developed by Kohonen. The theory of the SOM network is motivated by the observation of the operation of the brain. This paper presents the technique of SOM and shows how it may be applied as a clustering tool to group technology. A computer program for implementing the SOM neural networks is developed and the results are compared with other clustering approaches used in group technology. The study demonstrates the potential of using the Self-Organizing Map as the clustering tool for part family formation in group technology.     

An unsupervised method of classifying remotely sensed images using Kohonen self‐organizing maps and agglomerative hierarchical clustering methods

Unlike conventional unsupervised classification methods, such as K‐means and ISODATA, which are based on partitional clustering techniques, the methodology proposed in this work attempts to take advantage of the properties of Kohonen's self‐organizing map (SOM) together with agglomerative hierarchical clustering methods to perform the automatic classification of remotely sensed images. The key point of the proposed method is to execute the cluster analysis process by means of a set of SOM prototypes, instead of working directly with the original patterns of the image. This strategy significantly reduces the complexity of the data analysis, making it possible to use techniques that have not normally been considered viable in the processing of remotely sensed images, such as hierarchical clustering methods and cluster validation indices. Through the use of the SOM, the proposed method maps the original patterns of the image to a two‐dimensional neural grid, attempting to preserve the probability distribution and topology of the input space. Afterwards, an agglomerative hierarchical clustering method with restricted connectivity is applied to the trained neural grid, generating a simplified dendrogram for the image data. Utilizing SOM statistic properties, the method employs modified versions of cluster validation indices to automatically determine the ideal number of clusters for the image. The experimental results show examples of the application of the proposed methodology and compare its performance to the K‐means algorithm.     

A new approach for data clustering and visualization using self-organizing maps

A self-organizing map (SOM) is a nonlinear, unsupervised neural network model that could be used for applications of data clustering and visualization. One of the major shortcomings of the SOM algorithm is the difficulty for non-expert users to interpret the information involved in a trained SOM. In this paper, this problem is tackled by introducing an enhanced version of the proposed visualization method which consists of three major steps: (1) calculating single-linkage inter-neuron distance, (2) calculating the number of data points in each neuron, and (3) finding cluster boundary. The experimental results show that the proposed approach has the strong ability to demonstrate the data distribution, inter-neuron distances, and cluster boundary, effectively. The experimental results indicate that the effects of visualization of the proposed algorithm are better than that of other visualization methods. Furthermore, our proposed visualization scheme is not only intuitively easy understanding of the clustering results, but also having good visualization effects on unlabeled data sets.     

Recognition of Western style musical genres using machine learning techniques

This study uses machine learning techniques (ML) to classify and cluster different Western music genres. Three artificial neural network models (multi-layer perceptron neural network [MLP], probabilistic neural network [PNN]) and self-organizing maps neural network (SOM) along with support vector machines (SVM) are compared to two standard statistical methods (linear discriminant analysis [LDA] and cluster analysis [CA]). The variable sets considered are average frequencies, variance frequencies, maximum frequencies, amplitude or loudness of the sound and the median of the location of the 15 highest peaks in the periodogram. The results show that machine learning models outperform traditional statistical techniques in classifying and clustering different music genres due to their robustness and flexibility of modeling algorithms. The study also shows how it is possible to identify various dimensions of music genres by uncovering complex patterns in the multidimensional data.     

基于SOM网络的股票聚类分析方法

无监督的自组织映射(SOM)神经网络是用于聚类的主要人工神经网络模型之一.在SOM网络的基础上改进了网络中的邻域函数,并将其用于对股票进行分析和选择,得到了令人满意的结果.为了提高解的精度,避免多个输入样本映射到同一输出节点还提出了禁忌映射的方法.数值模拟表明该模型对于上市公司的聚类结果令人满意,对于股民客观、准确地选出真正具有投资价值的股票具有指导意义.     

Selecting the right MBA schools – An application of self-organizing map networks

The self-organizing map (SOM) network, an unsupervised neural computing network, is a categorization network developed by Kohonen. The SOM network was designed for solving problems that involve tasks such as clustering, visualization, and abstraction. In this study, we apply the clustering and visualization capabilities of SOM to group and plot the top 79 MBA schools as ranked by US News and World Report (USN&WR) into a two-dimensional map with four segments. The map should assist prospective students in searching for the MBA programs that best meet their personal requirements. Comparative analysis with the outputs from two popular clustering techniques K-means analysis and a two-step Factor analysis/K-means procedure are also included.     

基于遗传算法和自组织特征映射网络的文本聚类方法

自组织映射(SOM)算法作为一种聚类和高维可视化的无监督学习算法,为进行中文Web文档聚类提供了有力的手段。但是SOM算法天然存在着对网络初始权值敏感的缺陷,从而影响聚类质量。为此,引进遗传算法对SOM网络加以优化。提出了以遗传算法优化SOM网络的文本聚类算法（GSTCA）;进行了对比实验,实验表明,改进后的算法GSTCA比SOM算法在Web中文文档聚类中具有更高的准确率,其F-measure值平均提高了14%,同时,实验还表明,GSTCA算法对网络初始权值是不敏感的,从而提高了算法的稳定性。