A flexible multi-layer self-organizing map for generic processing of tree-structured data

A new multi-layer self-organizing map (MLSOM) is proposed for unsupervised processing tree-structured data. The MLSOM is an improved self-organizing map for handling structured data. By introducing multiple SOM layers, the MLSOM can overcome the computational speed and visualization problems of SOM for structured data (SOM-SD). Node data in different levels of a tree are processed in different layers of the MLSOM. Root nodes are dedicatedly processed on the top SOM layer enabling the MLSOM a better utilization of SOM map compared with the SOM-SD. Thus, the MLSOM exhibits better data organization, clustering, visualization, and classification results of tree-structured data. Experimental results on three different data sets demonstrate that the proposed MLSOM approach can be more efficient and effective than the SOM-SD.     

基于SOM的无线通信话务量最优加权组合预测

针对单个预测模型难以准确刻画无线通信话务量的演变规律,并考虑数据自身的多样性,提出了基于自组织映射（Self-Organizing Maps,SOM）神经网络的无线通信话务量最优加权组合预测方法。该方法利用SOM神经网络对话务量数据进行自动聚类,并对聚类后的每类数据,分别确定相应最优加权组合预测的权重,进而获得相应的预测值。实验结果表明,所提出方法不仅能提高话务量预测的精度,还能增强预测系统的稳定性。     

烟叶分类的神经网络方法

为实现烟叶nic—ts—rs—k—cl含量分布的自组织分类,用SOM神经网络技术,以山东中烟公司提供的129种烟叶为学习和测试样本,训练和检测神经网络对烟叶nic—ts—rs—k—cl含量分布的自组织分类。实验显示：经过1000步的训练,SOM神经网络矩阵映射重复操作归类相同率,在剔除特异结果后达到80％以上。这一结果证实,利SOM神经网络技术,可以对烟叶参数组合取值分布进行客观分类,为烟叶质量评级,属性预测、缺陷诊断,状态预测和种类识别提供依据。     

融合差分进化和SOM的组合文本聚类算法

自组织映射算法是一种重要的聚类模型,能够有效提高搜索引擎的精确性。为克服自组织映射网络对于初始连接权值敏感的不足,提出一种改进的差分进化和SOM相结合的组合文档聚类算法IDE-SOM,首先引入一种改进的差分进化算法对文档集进行一次粗聚类,旨在对SOM网络的初始连接权值进行优化,然后将这个连接权值初始化SOM网络进行细聚类。仿真实验表明,该算法在F-measure、熵等评价指标上都获得了较好的聚类效果。     

基于SOM聚类和灰度TOPSIS评价法的岩爆预测

随着资源量的不断减少,采矿深度不断增大,岩爆发生概率也逐渐增大。为对岩爆等级进行精确预测,提出了一种聚类-关联度TOPSIS模型预测岩爆的方法。在综合分析岩爆产生条件的基础上,从应力、岩性、能量三个指标对样本进行归类处理,并将灰色关联度和TOPSIS评价法相结合。该方法可以通过自组织特征映射网络将样本准确分类,同时通过灰色关联度计算不同指标的权值,最后通过TOPSIS评价法对岩爆等级进行判据。该方法使得岩爆预测多信息融合更加客观,可操作性强。对比工程实例,发现SOM神经网络聚类-关联度TOPSIS岩爆预测法模拟计算与工程实例情况基本一致。     

鄂尔多斯盆地碳酸盐岩储层测井产能预测研究

讨论了以模糊理论为基础的合试层产能分权法。由SOM自组织映射与TFI模糊聚类协同确定分类数及变量组合，以隶属度分权、动态地剖分合试层产能为预测单层样本数据体，以遗传神经网络建立预测模型来预测储层自然产能，形成独特、灵活的泛合试层测井产能预测方法。经对鄂尔多斯盆地中部气田16口井52个测试层的应用，取得较好的应用效果。     

A Novel Measure for Quantifying the Topology Preservation of Self-Organizing Feature Maps

Recently, feature maps have been applied to various problem domains. The success of some of these applications critically depends on whether feature maps are topologically ordered. In this paper, we propose a novel measure for quantifying the neighborhood preserving property of feature maps. Two data sets were tested to illustrate the performance of the proposed method. This revised version was published online in June 2006 with corrections to the Cover Date.     

Mining of Moving Objects from Time-Series Images and its Application to Satellite Weather Imagery

The framework of mining of moving objects from image data sequence is presented. Scenes are first clustered and labeled by using two-stage SOM that is modified to recognize images including similar moving objects as the same cluster, and that well recognizes scenes including prominent objects. After extraction of images which include prominent objects based on clustering result, the position and the shape of objects are approximated by using mixture gaussian model via EM algorithm, providing the adequate or larger number of components. By adopting the average of the data points in the smaller blocks as the initial parameters, the solutions are stabilized and the identification of components among time-series images and the tracking of a specific object become easier.This framework is applied to a four-year (ranging from 1997 to 2000) dataset of cloud images taken by Japanese weather satellite GMS-5 to evaluate its performance. Modified SOM method well classifies scenes which include prominent moving object, and seasonal variation tendency is detected in the cluster ID sequence. The result of object detection via EM algorithm for summer-type images including clear cloud masses such as typhoons shows that this approach well approximate the adequate distribution of cloud masses in many cases. Objects in the very irregular shapes are also well represented as the mixtures of gaussians.The extracted object information, together with the scene clustering result, is expected to offer us a rich source for knowledge discovery of video datasets. This approach is one of the effective ways of mining video images whose characteristics are unknown in advance, and thus applicable to the various type of applications.