核自组织映射竞争聚类

基于核方法可在高维特征空间中完成数据聚类,但缺乏对原输入空间聚类中心及结果的直观刻画.提出一种核自组织映射竞争聚类算法.该算法是利用核的特征,导出SOM算法的获胜神经元及权重更新规则,而竞争学习机制依然保持在原输入空间中,这样既解决了当输入样本分布结构呈高度非线性时,其分类能力下降的问题,而且解决了Donald[1]算法导致的特征空间中的获胜神经元在原始输入空间中的原像不存在,而无法对聚类结果利用可视化技术进行解释的问题.实验结果表明,提出的核自组织映射竞争聚类算法在某些数据集中可以获得比SOM算法更好的结果.     

基于多维自组织特征映射的聚类算法研究

作为神经网络的一种方法,自组织特征映射在数据挖掘、模式分类和机器学习中得到了广泛应用.本文详细讨论了自组织特征映射的聚类算法的工作原理和具体实现算法.通过系统仿真实验分析,SOFMF算法很好地克服了许多聚类算法存在的问题,在时间复杂度上具有良好的性能.     

自组织映射(SOM)聚类算法的研究

通过自组织映射神经网络实现的聚类算法能将任意维数的输入信号模式转变为一维或二维的离散映射,以拓扑有序的方式自适应实现这个变换.介绍自组织映射聚类算法的原理,通过实验进行仿真,结果表明自组织映射聚类算法是可行有效的.     

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。     

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。     

一种基于改进的自组织特征映射网络的文档聚类方法

采用标准的自组织特征映射(Self—OrganizingFeatureMap)网络进行文档分类时存在着收敛速度慢、不同的初始条件及学习样本输入顺序影响学习过程和学习结果等缺点。针对这种情况,论文提出了一种改进的自组织特征映射网络文档分类方法,即采用自适应的方法建立网络拓扑结构,利用输入训练样本来确定网络中的连接权值,综合系统能量函数和训练次数作为判断网络的学习结束标准,并且取得了比较好的实验结果。     

基于潜在语义分析和自组织特征映射神经网络的文本聚类研究

基于潜在语义分析和自组织特征映射神经网络（LSA—SOM）,本文提出一种文本聚类方法。采用潜在语义分析的理论表示文本特征向量,以体现特征词的语义关系并实现特征向量的降维。利用SOM网络算法进行无监督自组织学习,并通过不断调节网络节点间的权向量来实现文本聚类。该方法不必预先给定聚类个数,可以在任意合适的位置生成一个新的类,克服传统方法中文本种类需要预先给定的缺点。     

基于涌现自组织映射的聚类分析与可视化处理

K0honen自组织特征映射可实现高维模式空间到低维拓扑结构的映射,借此可进行模式聚类分析及高维数据的二维可视化.但当输入样本数目较多、复杂度较大时,采用KSOM将使相邻类簇间发生大面积重叠,降低聚类效果.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.测试结果表明.借助ESOM模型进行数据的聚类分析与可视化在诸多方面表现出优越的性能.     

基于自组织特征映射的图像压缩技术

计算机人工神经网络技术提供了新的图像压缩方法。自组织特征映射人工神经网络就能够用于图像的有损压缩。通过将图像分成若干小块,然后使用神经网络进行训练达到特征向量自动聚类,从而将这若干个图像块分成不同的类,其类别个数远小于图像块的个数,最后使用一个映射表保存这些信息。该方式,将图像中相同或者非常相似的部分归为一类,降低了信息冗余度,从而可以进行图像的有损压缩。该方法采用了计算机神经网络,有比较好的适应性,能够方便的和其它压缩技术结合实现效果更好的混合压缩,具有良好的推广价值。     

基于混合聚类和自组织映射的异常检测模型

针对异常检测方法中存在的异常连接信息不足的问题设计了一种基于混合聚类和自组织映射的异常检 测模型．首先提出了一种聚类算法用以进行异常检测,然后再通过自组织映射（SOM）对检测出的异常连接进行分 类以获得其更多的异常连接信息．最后应用实验数据集进行仿真,结果表明本检测模型是有效的,能够将检测到的 异常连接进行分类并由其所属类别给出该异常连接的更多信息,且检测和分类的效率比较高,误报率低．?     

自组织特征映射网络在故障诊断中的应用

运用自组织特征映射神经网络的工作原理和具体实现算法进行故障诊断分析,在对已有神经网络聚类分析方法概括和总结的基础上,结合实验数据、仿真数据对自组织特征映射算法故障模型诊断进行研究,得出了有意义的结论.     

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.     

Fast self-organizing feature map algorithm

We present an efficient approach to forming feature maps. The method involves three stages. In the first stage, we use the K-means algorithm to select N(2) (i.e., the size of the feature map to be formed) cluster centers from a data set. Then a heuristic assignment strategy is employed to organize the N(2) selected data points into an NxN neural array so as to form an initial feature map. If the initial map is not good enough, then it will be fine-tuned by the traditional Kohonen self-organizing feature map (SOM) algorithm under a fast cooling regime in the third stage. By our three-stage method, a topologically ordered feature map would be formed very quickly instead of requiring a huge amount of iterations to fine-tune the weights toward the density distribution of the data points, which usually happened in the conventional SOM algorithm. Three data sets are utilized to illustrate the proposed method.     

基于特征关系的加权投票聚类集成研究

对于具有多特征的复杂数据,使用子数据集作为聚类成员的输入并使用加权投票的聚类集成方法可以权衡不同聚类成员的质量,提高聚类的准确性和稳定性。针对子数据集的选择及权重的计算方式,提出了最小相关特征的子数据集选取方法,并基于特征关系分析比较了五种聚类成员的权重计算方法。实验结果表明,使用最小相关特征法选择每个聚类成员的输入数据,相比随机抽样法可提高聚类集成的准确率。基于五种权重计算方法的聚类集成准确率都比单聚类高,且时间消耗有明显差异。     

自组织神经网络的参数自适应方法

自组织神经网络的主要目的是将任意维数的输入信号模式转变成为一维或二维的离散映射,并且以拓扑有序的方式自适应地实现这个过程。学习过程中,对邻域宽度函数和学习率函数参数是根据经验选择的,没有一定的规则或方法,因此,邻域保持映射的获得往往先于参数的学习过程。将线性Kalman滤波器和基于无先导变换的Kalman滤波器分别用于学习率函数和邻域宽度函数的预测,可以提高自组织神经网络的学习能力。改进后的算法可以根据输入数据自适应地调整邻域宽度函数和学习率函数。     

Constrained self-organizing feature map to preserve feature extraction topology

In many classification problems, it is necessary to consider the specific location of an n-dimensional space from which features have been calculated. For example, considering the location of features extracted from specific areas of a two-dimensional space, as an image, could improve the understanding of a scene for a video surveillance system. In the same way, the same features extracted from different locations could mean different actions for a 3D HCI system. In this paper, we present a self-organizing feature map able to preserve the topology of locations of an n-dimensional space in which the vector of features have been extracted. The main contribution is to implicitly preserving the topology of the original space because considering the locations of the extracted features and their topology could ease the solution to certain problems. Specifically, the paper proposes the n-dimensional constrained self-organizing map preserving the input topology (nD-SOM-PINT). Features in adjacent areas of the n-dimensional space, used to extract the feature vectors, are explicitly in adjacent areas of the nD-SOM-PINT constraining the neural network structure and learning. As a study case, the neural network has been instantiate to represent and classify features as trajectories extracted from a sequence of images into a high level of semantic understanding. Experiments have been thoroughly carried out using the CAVIAR datasets (Corridor, Frontal and Inria) taken into account the global behaviour of an individual in order to validate the ability to preserve the topology of the two-dimensional space to obtain high-performance classification for trajectory classification in contrast of non-considering the location of features. Moreover, a brief example has been included to focus on validate the nD-SOM-PINT proposal in other domain than the individual trajectory. Results confirm the high accuracy of the nD-SOM-PINT outperforming previous methods aimed to classify the same datasets.

     

Clustering and visualization of bankruptcy trajectory using self-organizing map

Bankruptcy trajectory reflects the dynamic changes of financial situation of companies, and hence make possible to keep track of the evolution of companies and recognize the important trajectory patterns. This study aims at a compact visualization of the complex temporal behaviors in financial statements. We use self-organizing map (SOM) to analyze and visualize the financial situation of companies over several years through a two-step clustering process. Initially, the bankruptcy risk is characterized by a feature self-organizing map (FSOM), and therefore the temporal sequence is converted to the trajectory vector projected on the map. Afterwards, the trajectory self-organizing map (TSOM) clusters the trajectory vectors to a number of trajectory patterns. The proposed approach is applied to a large database of French companies spanning over four years. The experimental results demonstrate the promising functionality of SOM for bankruptcy trajectory clustering and visualization. From the viewpoint of decision support, the method might give experts insight into the patterns of bankrupt and healthy company development.     

Extraction of rice-planted area using a self-organizing feature map

We introduce a neural network of self-organizing feature map (SOM) to classify remote-sensing data, including microwave and optical sensors, for the estimation of areas of planted rice. This method is an unsupervised neural network which has the capability of nonlinear discrimination, and the classification function is determined by learning. The satellite data are observed before and after rice planting in 1999. Three sets of RADARSAT and one set of SPOT/HRV data were used in Higashi–Hiroshima, Japan. The RADARSAT image has only one band of data and it is difficult to extract the rice-planted area. However, the SAR back-scattering intensity in a rice-planted area decreases from April to May and increases from May to June. Therefore, three RADARSAT images from April to June were used in this study. The SOM classification was applied the RADARSAT and SPOT data to evaluate the rice-planted area estimation. It is shown that the SOM is useful for the classification of satellite data.     

Some novel classifiers designed using prototypes extracted by a newscheme based on self-organizing feature map

We propose two new comprehensive schemes for designing prototype-based classifiers. The scheme addresses all major issues (number of prototypes, generation of prototypes, and utilization of the prototypes) involved in the design of a prototype-based classifier. First we use Kohonen's self-organizing feature map (SOFM) algorithm to produce a minimum number (equal to the number of classes) of initial prototypes. Then we use a dynamic prototype generation and tuning algorithm (DYNAGEN) involving merging, splitting, deleting, and retraining of the prototypes to generate an adequate number of useful prototypes. These prototypes are used to design a "1 nearest multiple prototype (1-NMP)" classifier. Though the classifier performs quite well, it cannot reasonably deal with large variation of variance among the data from different classes. To overcome this deficiency we design a "1 most similar prototype (1-MSP)" classifier. We use the prototypes generated by the SOFM-based DYNAGEN algorithm and associate with each of them a zone of influence. A norm (Euclidean)-induced similarity measure is used for this. The prototypes and their zones of influence are fine-tuned by minimizing an error function. Both classifiers are trained and tested using several data sets, and a consistent improvement in performance of the latter over the former has been observed. We also compared our classifiers with some benchmark results available in the literature.     

Emergent self-organizing feature map for recognizing road sign images

Road sign recognition system remains a challenging part of designing an Intelligent Driving Support System. While there exist many approaches to classify road signs, none have adopted an unsupervised approach. This paper proposes a way of Self-Organizing feature mapping for recognizing a road sign. The emergent self-organizing map (ESOM) is employed for the feature mapping in this study. It has the capability of visualizing the distance structures as well as the density structure of high-dimensional data sets, in which the ESOM is suitable to detect non-trivial cluster structures. This paper discusses the usage of ESOM for road sign detection and classification. The benchmarking against some other commonly used classifiers was performed. The results demonstrate that the ESOM approach outperforms the others in conducting the same simulations of the road sign recognition. We further demonstrate that the result obtained with ESOM is significantly more superior than traditional SOM which does not take into the boundary effect like ESOM did.