一种生长型自组织神经网络的聚类研究

自组织特征映射神经网络SOM(Self-Organizing Feature Maps)是一种优良的聚类工具,但其存在着一些限制,如需要预先定义网络大小、网络的收敛性较差和结构不灵活等.为了克服这些不足,在自组织神经网络理论的指导下,提出了一种基于生长型自组织神经网络的聚类方法.在无监督的情况下,该方法采用阈值控制的触发机制实现网络中神经元的生长和删除,并通过神经元权值的有效调整,以期得到数据对象的聚类结果.实验以二维空间中的数据对象为输入样本,验证了该方法的有效性和优越性.     

自组织映射网络中数种拓扑函数的建立与应用

在人工神经网络中，拓扑函数用于生成神经元的空间拓扑结构。不同的拓扑函数可能导致神经网络形成不同的学习结果。以Matlab的拓扑函数模板为基础，建立了四种拓扑函数：cossintopf、sincostopf、acossintopf及expsintopf。同时，以这些拓扑函数生成神经元拓扑结构，用于自组织特征映射网络对田间无脊椎动物目的无监督自组织聚类。结果显示，聚类各有差异，可根据与实际情形的比较来选择拓扑函数与聚类结果，或进行综合分析。     

一种基于模糊聚类的文本挖掘新方法*

提出了一种新的动态模糊自组织神经网络模型(TGFCM),并将其用于文本聚类中。针对传统模糊自组织神经网络需要预先确定聚类数的问题,TGFCM采用了可自动确定聚类数的动态自组织神经网络(TGSOM)的结构,在TGSOM网络结构中提出新的学习率计算式,并以模糊聚类中心作为TGFCM网络中对应的神经元的权值,从而提高了聚类的精度,并可提高收敛速度。     

基于神经网络的抽油机故障诊断

自组织特征映射网络（Self-Organization Map,SOM）是一种具有强大的自学习功能、良好的自组织性和自适应性的神经网络,能够将输入数据映射到输出神经元阵列中,并保持数据集的拓扑结构和密度分布不变。针对基于传统竞争神经网络的抽油机示功图聚类时分类精度低的缺点,对自组织特征映射神经网络的学习速率和临域的递减方式进行改进,并在Matlab环境下进行仿真验证。仿真结果表明,本文所采取的改进能有效的提高聚类的精度和准确性。     

一种新聚类算法在基因表达数据分析中的应用

自组织特征映射神经网络与层次聚类算法是两种较经典的分析基因表达数据的聚类算法,但由于基因表达数据的复杂性与不稳定性,这两种算法都存在着自身的优劣。因此,在比较两种算法差异性的基础上,创造性地提出了一种新算法,即通过SOM算法对基因表达数据进行聚类,再用层次聚类将每个类对应的神经元权值二次聚类,并将此算法应用在酵母菌基因表达数据中,用实验证明改进算法克服了自组织算法的一些缺陷,提高了基因聚类的效能。     

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。     

改进的Kohonen神经网络航迹关联算法

针对传统的航迹关联算法在运动目标交叉、分岔时,常出现错漏相关航迹且计算量随着传感器和目标数量增加而飞速增长的缺陷,提出一种改进的Kohonen神经网络航迹关联算法。该算法由聚类关联、目标状态估计、神经元优化和状态融合估计等模块组成。通过给每个竞争层神经元加上一个合适的阈值,有效避免了常规的Kohonen神经网络因初始权值选择不合适而容易造成坏死神经元的问题。进一步设计了自组织竞争神经网络学习规则,将多传感器在同一时刻的测量数据进行自组织聚类,从而实现测量数据的有效关联。最后,利用连续时间下的关联数据,实现运动目标航迹关联。仿真研究验证了该算法的可行性和有效性。     

一种基于改进神经网络的高效模糊聚类算法

针对利用自组织特征映射(SOFM)神经网络进行模糊聚类时出现的一些问题,提出改进结构的神经网络,采用自适应的聚类初值,能够实现高维数据和任意形状族的聚类,与具有同样聚类效果的其他算法相比,具有较低的时间复杂度。仿真实验结果表明,该聚类算法比单个的神经网络聚类算法和同类其他算法更有效。     

一种新的模糊规则提取方法

提出了一种新的模糊规则提取方法,该方法先采用基于山峰函数的减法聚类法自适应地确定初始的聚类中心,然后由此构造动态自组织神经网络进行学习,在学习的过程中可根据情况适当地合并或分裂神经元,并重构神经网络继续学习,最后按聚类中心确定模糊子集数目和隶属函数并形成模糊规则集.实验结果表明,通过网络结构和神经元的动态自适应变化能够获取样本集中的模糊信息,形成直观的模糊规则.     

基于自组织神经网络的QRS波聚类方法

为提高查看大量数据动态心电(ECG)图时的效率,将波形聚类,采用埃尔米特函数和自组织神经网络,实现了室性早搏占比高情况下的心电波形聚类算法.使用MIT-BIH心率失常数据库,利用埃尔米特函数分解QRS波形为QRS向量,将所有QRS向量输入自组织神经网络进行分类.使用特征向量元素分析聚类结果,用阳性率指标对结果进行统计,平均真阳性率为91.2％,假阳性率为1.03％,验证了基于自组织神经网络的心电聚类算法的有效性.达到了将正常心搏和室性早搏心搏聚类的目标.     

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.     

A SOM prototype-based cluster analysis methodology

Data clustering is aimed at finding groups of data that share common hidden properties. These kinds of techniques are especially critical at early stages of data analysis where no information about the dataset is available. One of the mayor shortcomings of the clustering algorithms is the difficulty for non-experts users to configure them and, in some cases, interpret the results. In this work a computational approach with a two-layer structure based on Self-Organizing Map (SOM) is presented for cluster analysis. In the first level, a quantization of the data samples using topology-preserving metrics to automatically determine the number of units in the SOM is proposed. In the second level the obtained SOM prototypes are clustered by means of a connectivity analysis to explore the quality of the partitioning with different number of clusters. The most important benefit of this two-layer procedure is that computational load decreases considerably in comparison with data based clustering methods, making it possible to cluster large data sets and to consider several different clustering alternatives in a limited time. This methodology produces a two-dimensional map representation of the, usually, high dimensional input space, along with quantitative information on viable clustering alternatives, which facilitates the exploration of the possible partitions in a dataset. The efficiency and interpretation of the methodology is illustrated by its application to artificial, benchmark and real complex biological datasets. The experimental results demonstrate the ability of the method to identify possible segmentations in a dataset, compared to algorithms that only yield a single clustering solution. The proposed algorithm tackles the intrinsic limitations of SOM and the parameter settings associated with the clustering methodology, without requiring the number of clusters or the SOM architecture as a prerequisite, among others. This way, it makes possible its application even by researchers with a limited expertise in machine learning.     

自组织映射拓扑保持的增强

在自组织映射(SOM)中,网格各单元的权值向量仅仅是根据各单元和最佳匹配单元(BMU)之间的距离进行更新的,因而输入数据间的拓扑关系不能得到很好的保持。为此提出了两种改进方案。在第一种改进方案中,各单元的权值向量根据各单元和BMU之间对应各坐标的差进行更新。实验结果表明,这种改进方案可以很好地保持拓扑关系,但输入数据的分布密度却不能得到较好的体现。在第二种改进方案中,各单元的权值向量同时根据各单元和BMU之间对应各坐标的差与距离进行更新。实验结果表明,这种改进方案不仅能使拓扑关系得到比SOM更好的保持,而且较好地体现了输入数据的分布密度,并加快了训练的收敛速度。     

Visual feature analysis by the self-organising maps

The Self-Organising Map (SOM) is an Artificial Neural Network (ANN) model consisting of a regular grid of processing units. A model of some multidimensional observation, e.g. a class of digital images, is associated with each unit. The map attempts to represent all the available observations using a restricted set of models. In unsupervised learning, the models become ordered on the grid so that similar models are close to each other. We review here the objective functions and learning rules related to the SOM, starting from vector coding based on a Euclidean metric and extending the theory of arbitrary metrics and to a subspace formalism, in which each SOM unit represents a subspace of the observation space. It is shown that this Adaptive-Subspace SOM (ASSOM) is able to create sets of wavelet- and Gabor-type filters when randomly displaced or moving input patterns are used as training data. No analytical functional form for these filters is thereby postulated. The same kind of adaptive system can create many other kinds of invariant visual filters, like rotation or scale-invariant filters, if there exist corresponding transformations in the training data. The ASSOM system can act as a learning feature-extraction stage for pattern recognisers, being able to adapt to arbitrary sensory environments. We then show that the invariant Gabor features can be effectively used in face recognition, whereby the sets of Gabor filter outputs are coded with the SOM and a face is represented by the histogram over the SOM units.     

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。     

On robustness of radial basis function network with input perturbation

In this article, we have proposed a methodology for making a radial basis function network (RBFN) robust with respect to additive and multiplicative input noises. This is achieved by properly selecting the centers and widths for the radial basis function (RBF) units of the hidden layer. For this purpose, firstly, a set of self-organizing map (SOM) networks are trained for center selection. For training a SOM network, random Gaussian noise is injected in the samples of each class of the data set. The number of SOM networks is same as the number of classes present in the data set, and each of the SOM networks is trained separately by the samples belonging to a particular class. The weight vector associated with a unit in the output layer of a particular SOM network corresponding to a class is used as the center of a RBF unit for that class. To determine the widths of the RBF units, p-nearest neighbor algorithm is used class-wise. Proper selection of centers and widths makes the RBFN robust with respect to input perturbation and outliers present in the data set. The weights between the hidden and output layers of RBFN are obtained by pseudo inverse method. To test the robustness of the proposed method in additive and multiplicative noise scenarios, ten standard data sets have been used for classification. Proposed method has been compared with three existing methods, where the centers have been generated in three ways: randomly, using k-means algorithm, and based on SOM network. Simulation results show the superiority of the proposed method compared to those methods. Wilcoxon signed-rank test also shows that the proposed method is statistically better than those methods.

     

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。     

A visual-inspection system using a self-organizing map

Visual inspections by hand often cause bottlenecks in production processes in industries. Therefore, it is desirable to be mechanized and automated. In order to satisfy these requirements, we apply image recognition using a self-organizing map (SOM) to visual inspection equipment. The SOM maps high-dimensional input data onto a low-dimensional (typically two-dimensional) space. Through the mapping, the data are automatically clustered based on their similarity. Any unknown data which are input onto the self-organized map are also mapped onto it according to their similarity. The categories of the unknown data are thus recognized based on their positions on the map. The reason we use a SOM for inspections is that users can then know the similarity distribution of all data at a glance on the map, and understand the mechanism of the recognition visually. We have developed a visual inspection system using a SOM, and have evaluated it using actual product images. We have obtained high recognition accuracies of 98% and 96% for one- and two-inspection-point tests, respectively, for a real industrial product.     

一种自动抽取图像中可判别区域的新方法

图像分割是图像处理中的一个难题，为了自动抽取图像中的可差别区域，提出了一种基于自组织图归约算法的区域抽取新方法，首先，利用包括颜色、纹理以及位置在内的多模特征抽算法，原始图像被转换成特征，接着，通过自组织映射学习算法，特征图映射成自组织图，然后，对自组织图实施归纳算法得到一族约简的自组织图谱系；最后，利用一个 综合的聚类有效性分析指标从约简的自组织图谱系中得到一个最优约简的自组织图，以此实现图像区域的分割，新方法的有效性通过两个评价实验得到了验证。     

Studying the feasibility of a recommender in a citizen web portal based on user modeling and clustering algorithms

This paper presents a methodology to estimate the future success of a collaborative recommender in a citizen web portal. This methodology consists of four stages, three of them are developed in this study. First of all, a user model, which takes into account some usual characteristics of web data, is developed to produce artificial data sets. These data sets are used to carry out a clustering algorithm comparison in the second stage of our approach. This comparison provides information about the suitability of each algorithm in different scenarios. The benchmarked clustering algorithms are the ones that are most commonly used in the literature: c-Means, Fuzzy c-Means, a set of hierarchical algorithms, Gaussian mixtures trained by the expectation-maximization algorithm, and Kohonen's self-organizing maps (SOM). The most accurate clustering is yielded by SOM. Afterwards, we turn to real data. The users of a citizen web portal (Infoville XXI, http://www.infoville.es) are clustered. The clustering achieved enables us to study the future success of a collaborative recommender by means of a prediction strategy. New users are recommended according to the cluster in which they have been classified. The suitability of the recommendation is evaluated by checking whether or not the recommended objects correspond to those actually selected by the user. The results show the relevance of the information provided by clustering algorithms in this web portal, and therefore, the relevance of developing a collaborative recommender for this web site.