竞争层结构可调SOM网络在中药模式识别中的应用

针对经典SOM算法无法准确反映原始数据的特征信息,提出了竞争层结构可调的SOM算法——CSA—SOM算法。该算法增加了竞争层神经元动态调节的步骤,调节的依据是不断比较原数据的位置信息和映射后低雏空间的位置信息,使两者最终能趋于一致。因此降维后的数据能够较好地保持原数据的特征,包括距离信息、角度信息以及分布信息。该算法有效地实现了红景天药材的准确清晰分类。算法理论分析和实验结果均表明,CSA—SOM算法是一种快速、准确的数据内在规律映射可视化算法,与SOM算法相比,CSA—SOM算法的特征映射效果比较好,解决了SOM算法会使映射后数据结构发生扭曲的问题。     

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。     

一种 SOM 和 K-Means 结合算法在 SMT 焊接质量中的应用研究

针对SOM 神经网络算法复杂度高精度低以及K-Means聚类算法需事先确定聚类（簇）数目和随机选取初始聚类中心的不足,论文提出了一种SOM神经网络与K-M eans相结合的S-K二次聚类算法,进行功能互补。该算法应用在SM T焊接质量上,能提高数据聚类信息的精确度,直观地看到数据的分布情况,改善系统的整体性能。     

基于SOM算法的文本聚类实现

以自组织映射（Self-organizing Map,SOM）算法作为理论基础,实现对文本聚类,并采用U矩阵进行可视化表示。通过对聚类结果的分析,表明SOM算法具有较好的聚类效果。     

利用SOM网络模型进行聚类研究

自组织特征映射（SOM）是Kohonen提出的一种人工神经网络模型,其整个学习过程是在输入样本空间内进行．并以欧氏距离为度量。本文先介绍了SOM网络模型的来源,接着对SOM网络的结构与学习过程进行了介绍,最后给出了一个SOM网络模型在聚类中的程序实例。     

基于SOM网络的语音训练可视化方法研究

提出了一种利用SOM网络输出层可视化的特点进行语音训练的方法。SOM网络能够将输入向量映射到二维平面或曲面上,受试者通过视觉反馈的位置信息,指导其发音行为。为了提高SOM聚类效果,SOM还进行加强训练;讨论了SOM输出层神经元个数对聚类的影响。实验结果表明,提出的利用SOM语音训练方法,直观简单,能够有效地实现“看图说话”。     

基于SOM的多层Web安全审计模型及其实现

随着Internet的普及，Web应用系统安全问题日益严重，而安全审计是保障信息系统安全的重要措施。可视化安全审计作为一种新的安全审计手段，有助于安全管理员充分理解主体行为，但在Web系统中却因运行效率等原因导致应用受到限制。本文定义了多层用户行为模型，以此模型作为Web安全审计的基础，并针对该行为模型提出了基于SOM的可视化算法。该算法将Web审计数据转换为形象的可视化信息，并且允许安全管理员对可视信息进行交互操作，进一步探索Web审计数据的内在关联，从而在保证可视化效果的同时，还可大幅提高安全审计的效率。     

基于SOM的模糊聚类在Web日志挖掘中的应用

将自组织映射神经网络（SOM）与FCM结合,利用SOM的并行计算能够减少模糊C均值算法在处理海量数据时的聚类时间,可以提高聚类算法的速度和效果,同时使用该算法对校园网Web日志进行数据挖掘,能够对用户行为进行分析,从而提出相应的方法,更好地提高服务效率和管理质量。     

一种有效的可视化孤立点发现与预测新途径

孤立点发现是数据挖掘活动的重要组成部分,被广泛应用于电子贸易、信用卡等领域的欺诈检测。由于优良的拓扑结构保持和概率分布保持特性,SOM（Self-Organizing Maps）可作为一种有效的降维工具供分析人员获取隐藏于数据中的分布结构信息。在分析了当前基于距离的孤立点发现的基础上,提出了一种基于SOM的孤立点发现与预测新途径,具有可扩展性、可预测性、交互性、简明性等特征。实验结果表明,基于SOM的孤立点发现与预测是有效的。     

基于自组织映射模型的数据挖掘算法在对二甲苯吸附分离过程中的应用

以PX吸附分离过程为研究对象，运用基于SOM模型的数据挖掘算法对其进行分析研究．SOM模型在整个挖掘过程中起了关键性的作用．一方面，SOM模型作为探索性数据分析的有效工具，为进一步的挖掘提供了依据．另一方面，SOM模型为聚类算法提供参数指导和数据支持．最终，通过数据挖掘实现了两个目标，得到了在不同负荷情况下操作参数的稳态优化区域；建立了可用于指导操作员改进操作的可视化实时评估模型．     

A self-organizing map for transactional data and the related categorical domain

After projecting high dimensional data into a two-dimension map via the SOM, users can easily view the inner structure of the data on the 2-D map. In the early stage of data mining, it is useful for any kind of data to inspect their inner structure. However, few studies apply the SOM to transactional data and the related categorical domain, which are usually accompanied with concept hierarchies. Concept hierarchies contain information about the data but are almost ignored in such researches. This may cause mistakes in mapping. In this paper, we propose an extended SOM model, the SOMCD, which can map the varied kinds of data in the categorical domain into a 2-D map and visualize the inner structure on the map. By using tree structures to represent the different kinds of data objects and the neurons’ prototypes, a new devised distance measure which takes information embedded in concept hierarchies into consideration can properly find the similarity between the data objects and the neurons. Besides the distance measure, we base the SOMCD on a tree-growing adaptation method and integrate the U-Matrix for visualization. Users can hierarchically separate the trained neurons on the SOMCD's map into different groups and cluster the data objects eventually. From the experiments in synthetic and real datasets, the SOMCD performs better than other SOM variants and clustering algorithms in visualization, mapping and clustering.     

基于自组织映射的流形学习与可视化

针对自组织映射(SOM)在学习和可视化高维数据内在的低维流形结构时容易产生“拓扑缺陷”的这一问题,提出了一种新的流形学习算法--动态自组织映射(DSOM)。该算法按照数据的邻域结构逐步扩展训练数据集合,对网络进行渐进训练,以避免局部极值,克服“拓扑缺陷”问题;同时,网络规模也随之动态扩展,以降低算法的时间复杂度。实验表明,该算法能更加真实地学习和可视化高维数据内在的低维流形结构;此外,与传统的流形学习算法相比,该算法对邻域大小和噪声也更加鲁棒。所提算法的网络规模和训练数据集合都将按照数据内在的邻域结构进行同步扩展,从而能更加简洁并真实地学习和可视化高维数据内在的低维流形结构。     

自组织映射网络的可视化研究

提出了一种自组织映射网络训练结果的可视化方法—距离映射法,该方法根据输入向量与竞争层神经元权向量距离的大小来计算相似度,然后对所有相似度与对应神经元坐标之积求和,把输入向量映射到二维平面。对故障数据的试验分析表明,该方法能提供更加清晰的可视化表示。     

Flexible array self-organizing map neural network and its application in complex pattern recognition

Due to the fixed array of competing-layer structure, observations distribution features cannot be well reflected by conventional SOM in two-dimensional plane. Aimed at solving this problem, a novel flexible array SOM algorithm (faSOM) is proposed in this paper. This algorithm can adaptively adjust the positions of competing-layer neurons to keep consistent with position of observations. As a result, the neurons in mapping space can maintain the original observation’ features. The faSOM algorithm is successfully applied in pattern recognition of two artificial datasets and red-spotted stonecrop samples. Both theory analysis and experimental results indicate that faSOM is an effective algorithm which can map observation’s inherent feature quickly and accurately. Compared with conventional SOM algorithm, feature mapping effect of faSOM algorithm is much better, because it resolves a typical problem in the conventional SOM that the structure of mapped dataset in competing-layer is distorted.     

PolSOM: A new method for multidimensional data visualization

In this paper, a new algorithm named polar self-organizing map (PolSOM) is proposed. PolSOM is constructed on a 2-D polar map with two variables, radius and angle, which represent data weight and feature, respectively. Compared with the traditional algorithms projecting data on a Cartesian map by using the Euclidian distance as the only variable, PolSOM not only preserves the data topology and the inter-neuron distance, it also visualizes the differences among clusters in terms of weight and feature. In PolSOM, the visualization map is divided into tori and circular sectors by radial and angular coordinates, and neurons are set on the boundary intersections of circular sectors and tori as benchmarks to attract the data with the similar attributes. Every datum is projected on the map with the polar coordinates which are trained towards the winning neuron. As a result, similar data group together, and data characteristics are reflected by their positions on the map. The simulations and comparisons with Sammon's mapping, SOM and ViSOM are provided based on four data sets. The results demonstrate the effectiveness of the PolSOM algorithm for multidimensional data visualization.     

ViSOM - a novel method for multivariate data projection andstructure visualization

When used for visualization of high-dimensional data, the self-organizing map (SOM) requires a coloring scheme, such as the U-matrix, to mark the distances between neurons. Even so, the structures of the data clusters may not be apparent and their shapes are often distorted. In this paper, a visualization-induced SOM (ViSOM) is proposed to overcome these shortcomings. The algorithm constrains and regularizes the inter-neuron distance with a parameter that controls the resolution of the map. The mapping preserves the inter-point distances of the input data on the map as well as the topology. It produces a graded mesh in the data space such that the distances between mapped data points on the map resemble those in the original space, like in the Sammon mapping. However, unlike the Sammon mapping, the ViSOM can accommodate both training data and new arrivals and is much simpler in computational complexity. Several experimental results and comparisons with other methods are presented.     

利用自组织特征映射神经网络进行可视化聚类

自组织特征映射作为一种神经网络方法,在数据挖掘、机器学习和模式分类中得到了广泛的应用。它将高维输人空间的数据映射到一个低维、规则的栅格上,从而可以利用可视化技术探测数据的固有特性。该文说明了自组织特征映射神经网络的工作原理和具体实现算法,同时利用一个算例展示了利用自组织特征映射进行聚类时的可视化特性,包括聚类过程的可视化和聚类结果的可视化,这也是自组织特征映射得到广泛应用的原因之一。     

Handling of incomplete data sets using ICA and SOM in data mining

Based on independent component analysis (ICA) and self-organizing maps (SOM), this paper proposes an ISOM-DH model for the incomplete data’s handling in data mining. Under these circumstances the data remain dependent and non-Gaussian, this model can make full use of the information of the given data to estimate the missing data and can visualize the handled high-dimensional data. Compared with mixture of principal component analyzers (MPCA), mean method and standard SOM-based fuzzy map model, ISOM-DH model can be applied to more cases, thus performing its superiority. Meanwhile, the correctness and reasonableness of ISOM-DH model is also validated by the experiment carried out in this paper.     

Generalizing self-organizing map for categorical data

The self-organizing map (SOM) is an unsupervised neural network which projects high-dimensional data onto a low-dimensional grid and visually reveals the topological order of the original data. Self-organizing maps have been successfully applied to many fields, including engineering and business domains. However, the conventional SOM training algorithm handles only numeric data. Categorical data are usually converted to a set of binary data before training of an SOM takes place. If a simple transformation scheme is adopted, the similarity information embedded between categorical values may be lost. Consequently, the trained SOM is unable to reflect the correct topological order. This paper proposes a generalized self-organizing map model that offers an intuitive method of specifying the similarity between categorical values via distance hierarchies and, hence, enables the direct process of categorical values during training. In fact, distance hierarchy unifies the distance computation of both numeric and categorical values. The unification is done by mapping the values to distance hierarchies and then measuring the distance in the hierarchies. Experiments on synthetic and real datasets were conducted, and the results demonstrated the effectiveness of the generalized SOM model.     

Clustering of the self-organizing map

The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. In this paper, different approaches to clustering of the SOM are considered. In particular, the use of hierarchical agglomerative clustering and partitive clustering using K-means are investigated. The two-stage procedure-first using SOM to produce the prototypes that are then clustered in the second stage-is found to perform well when compared with direct clustering of the data and to reduce the computation time.