基于GHSOM网络的时间序列聚类方法

提出了一种基于增长型分层自组织映射(GHSOM)的时间序列聚类方法，给出了该方法的基本原理和具体算法步骤，对实测时间序列数据进行了聚类验证和分析。研究结果表明，增长型分层自组织映射能根据对象特征无监督地对时间序列进行正确聚类，由于具有动态增长及分层特性，能分析对象内在的层次结构并实现由粗到精的聚类，可以扩展应用于大型乃至巨量时间序列数据库的模式发现。     

Hierarchical Color Quantization Based on Self-organization

In this paper, a new hierarchical color quantization method based on self-organizing maps that provides different levels of quantization is presented. Color quantization (CQ) is a typical image processing task, which consists of selecting a small number of code vectors from a set of available colors to represent a high color resolution image with minimum perceptual distortion. Several techniques have been proposed for CQ based on splitting algorithms or cluster analysis. Artificial neural networks and, more concretely, self-organizing models have been usually utilized for this purpose. The self-organizing map (SOM) is one of the most useful algorithms for color image quantization. However, it has some difficulties related to its fixed network architecture and the lack of representation of hierarchical relationships among data. The growing hierarchical SOM (GHSOM) tries to face these problems derived from the SOM model. The architecture of the GHSOM is established during the unsupervised learning process according to the input data. Furthermore, the proposed color quantizer allows the evaluation of different color quantization rates under different codebook sizes, according to the number of levels of the generated neural hierarchy. The experimental results show the good performance of this approach compared to other quantizers based on self-organization.     

Ranked centroid projection: a data visualization approach with self-organizing maps

The self-organizing map (SOM) is an efficient tool for visualizing high-dimensional data. In this paper, the clustering and visualization capabilities of the SOM, especially in the analysis of textual data, i.e., document collections, are reviewed and further developed. A novel clustering and visualization approach based on the SOM is proposed for the task of text mining. The proposed approach first transforms the document space into a multidimensional vector space by means of document encoding. Afterwards, a growing hierarchical SOM (GHSOM) is trained and used as a baseline structure to automatically produce maps with various levels of detail. Following the GHSOM training, the new projection method, namely the ranked centroid projection (RCP), is applied to project the input vectors to a hierarchy of 2-D output maps. The RCP is used as a data analysis tool as well as a direct interface to the data. In a set of simulations, the proposed approach is applied to an illustrative data set and two real-world scientific document collections to demonstrate its applicability.     

Comparison of visualization of optimal clustering using self-organizing map and growing hierarchical self-organizing map in cellular manufacturing system

The present research deals with the cell formation problem (CFP) of cellular manufacturing system which is a NP-hard problem thus, the development of optimum machine-part cell formation algorithms has always been the primary attraction in the design of cellular manufacturing system. In this proposed work, the self-organizing map (SOM) approach has been used which is able to project data from a high-dimensional space to a low-dimensional space so it is considered a visualized approach for explaining a complicated CFP data set. However, for a large data set with a high dimensionality, a traditional flat SOM seems difficult to further explain the concepts inside the clusters. We propose one such possible solution for a large CFP data set by using the SOM in a hierarchical manner known as growing hierarchical self-organizing map (GHSOM). In the present work, the two novel contributions using GHSOM are: the choice of optimum architecture through the minimum pattern units extracted at layer 1 for the respective threshold values and selection. Furthermore, the experimental results clearly indicated that the machine-part visual clustering using GHSOM can be successfully applied in identifying a cohesive set of part family that is processed by a machine group. Computational experience specifically with the proposed GHSOM algorithm, on a set of 15 CFP problems from the literature, has shown that it performs remarkably well. The GHSOM algorithm obtained solutions that are at least as good as the ones found the literature. For 75% of the cell formation problems, the GHSOM algorithm improved the goodness of cell formation through GTE performance measure using SOM as well as best one from the literature, in some cases by as much as more than 12.81% (GTE). Thus, comparing the results of the experiment in this paper with the SOM and GHSOM using the paired t-test it has been revealed that the GHSOM approach performed better than the SOM approach so far the group technology efficiency (GTE) measures of performance of the goodness of cell formation is concerned.     

基于改进的GHSOM网络预测客户欺诈行为

生长、分级的自组织映射(Growing Hierarchical Self-Organizing Map,GHSOM)网络是自组织映射(Self-Organizing Map,SOM)网络的一种变体,它不仅具备了SOM网络可解释性强的优点,同时采用多层分级的结构,不需要预先定义好网络的结构和尺寸,解决了SOM由于竞争层神经元过多造成的训练时间过长的问题,却忽略了对样本向量各个分量在模型中重要性的分析,因此将一种新的输入模式分量和映射单元权向量之间的灰关联度引入到网络权值的调整过程中,对GHSOM算法进行了改进。运用于对电信客户行为的分类,从中获取了预测欺诈客户的关键指标,大大降低了输入样本的维度。结果显示,采用改进后的GHSOM算法降维后,分类正确率仍然可以达到94.59%。     

Image Compression and Video Segmentation Using Hierarchical Self-Organization

Both image compression based on color quantization and image segmentation are two typical tasks in the field of image processing. Several techniques based on splitting algorithms or cluster analyses have been proposed in the literature. Self-organizing maps have been also applied to these problems, although with some limitations due to the fixed network architecture and the lack of representation in hierarchical relations among data. In this paper, both problems are addressed using growing hierarchical self-organizing models. An advantage of these models is due to the hierarchical architecture, which is more flexible in the adaptation process to input data, reflecting inherent hierarchical relations among data. Comparative results are provided for image compression and image segmentation. Experimental results show that the proposed approach is promising for image processing, and the powerful of the hierarchical information provided by the proposed model.     

Web mining based on Growing Hierarchical Self-Organizing Maps: Analysis of a real citizen web portal

This work is focused on the usage analysis of a citizen web portal, Infoville XXI (http://www.infoville.es) by means of Self-Organizing Maps (SOM). In this paper, a variant of the classical SOM has been used, the so-called Growing Hierarchical SOM (GHSOM). The GHSOM is able to find an optimal architecture of the SOM in a few iterations. There are also other variants which allow to find an optimal architecture, but they tend to need a long time for training, especially in the case of complex data sets. Another relevant contribution of the paper is the new visualization of the patterns in the hierarchical structure. Results show that GHSOM is a powerful and versatile tool to extract relevant and straightforward knowledge from the vast amount of information involved in a real citizen web portal.     

Different Aspects of Clustering The Self-Organizing Maps

Self-organizing map (SOM) is an artificial neural network tool that is trained using unsupervised learning to produce a low dimensional representation of the input space, called a map. This map is generally the object of a clustering analysis step which aims to partition the referents vectors (map neurons) into compact and well-separated groups. In this paper, we consider the problem of the clustering SOM using different aspects: partitioning, hierarchical and graph coloring based techniques. Unlike the traditional clustering SOM techniques, which use k-means or hierarchical clustering, the graph-based approaches have the advantage of providing a partitioning of the SOM by simultaneously using dissimilarities and neighborhood relations provided by the map. We present the experimental results of several comparisons between these different ways of clustering.     

一种基于半监督GHSOM的入侵检测方法

基于神经网络的入侵检测方法是入侵检测技术的一个重要发展方向.在已有无监督生长型分层自组织映射(growing hierarchical self-organizing maps, GHSOM)神经网络算法的基础上,提出了一种半监督GHSOM算法.该算法利用少量有标签的数据指导大规模无标签数据的聚类过程.一方面借鉴cop-kmeans半监督机制,解决了原始算法中返回空划分的问题,并将其应用到GHSOM算法中.另一方面提出了神经元信息熵的概念作为子网生长的判断条件,提高了GHSOM网络子网划分的精度.此外还利用有标签的数据自动确定聚类结果的入侵类型.对KDD Cup 1999数据集和LAN环境下模拟产生的数据集进行的入侵检测实验表明：相比于无监督的GHSOM算法,半监督的GHSOM算法对各种类型的攻击具有较高的检测率.     

GHSOM在遥感图像分割中的应用

提出一种基于自组织增长分级神经网络（Growing Hierarchical Self-Organizing Map ,GHSOM）的遥感图像分类方法。首先详细分析了GHSOM方法的基本原理和算法,然后成功将其应用于遥感图像分类。实验结果表明了GHSOM通过分级的分类方法有效解决了SOM分类中的混分问题,大大提高了分类精度和效率,是一种新的有效的无监督遥感图像分类方法。     

Combined use of supervised and unsupervised learning for power system dynamic security mapping

This paper proposes a new methodology which combines supervised and unsupervised learning for evaluating power system dynamic security. Based on the concept of stability margin, pre-fault power system conditions are assigned to the output neurons on the two-dimensional grid with the growing hierarchical self-organizing map technique (GHSOM) via supervised artificial neural networks (ANNs) which perform an estimation of post-fault power system state. The technique estimates the dynamic stability index that corresponds to the most critical value of synchronizing and damping torques of multimachine power systems. ANN-based pattern recognition is carried out with the growing hierarchical self-organizing feature mapping in order to provide adaptive neural network architecture during its unsupervised training process. Numerical tests, carried out on a IEEE 9 bus power system are presented and discussed. The analysis using such method provides accurate results and improves the effectiveness of system security evaluation.     

Self-organising swarm (SOSwarm)

This paper introduces a novel version of the particle swarm optimisation (PSO) algorithm which we call self-organising swarm SOSwarm. SOSwarm can be used for unsupervised learning. In the algorithm, input vectors are projected into a lower-dimensional map space producing a visual representation of the input data in a manner similar to a self-organising map (SOM). In SOSwarm, particles react to input data during the learning process by modifying their velocities using an adaptation of the PSO velocity update function. SOSwarm is successfully applied to ten benchmark problems drawn from the UCI Machine Learning repository. The paper also demonstrates how the canonical SOM can be explored within the PSO paradigm. Illustrating this linkage between the heretofore distinct literatures of SOM and PSO opens up several new avenues of research for the development of novel self-organising algorithms.     

Controlling the spread of dynamic self-organising maps

The growing self-organising map (GSOM) has recently been proposed as an alternative neural network architecture based on the traditional self-organising map (SOM). The GSOM provides the user with the ability to control the spread of the map by defining a parameter called the spread factor (SF), which results in enhanced data mining and hierarchical clustering opportunities. When experimenting with the SOM, the grid size (number of rows and columns of nodes) can be changed until a suitable cluster distribution is achieved. In this paper we highlight the effect of the spread factor on the GSOM and contrast this effect with grid size change (increase and decrease) in the SOM. We also present experimental results in support of our claims regarding differences between GSOM and SOM.     

MIGSOM: Multilevel Interior Growing Self-Organizing Maps for High Dimensional Data Clustering

Understanding the inherent structure of high-dimensional datasets is a very challenging task. This can be tackled from visualization, summarizing or simply clustering points of view. The Self-Organizing Map (SOM) is a powerful and unsupervised neural network to resolve these kinds of problems. By preserving the data topology mapped onto a grid, SOM can facilitate visualization of data structure. However, classical SOM still suffers from the limits of its predefined structure. Growing variants of SOM can overcome this problem, since they have tried to define a network structure with no need an advance a fixed number of output units by dynamic growing architecture. In this paper we propose a new dynamic SOMs called MIGSOM: Multilevel Interior Growing SOMs for high-dimensional data clustering. MIGSOM present a different architecture than dynamic variants presented in the literature. Using an unsupervised training process MIGSOM has the capability of growing map size from the boundaries as well as the interior of the network in order to represent more faithfully the structure present in a data collection. As a result, MIGSOM can have three-dimensional (3-D) structure with different levels of oriented maps developed according to data direction. We demonstrate the potential of the MIGSOM with real-world datasets of high-dimensional properties in terms of topology preserving visualization, vectors summarizing by efficient quantization and data clustering. In addition, MIGSOM achieves better performance compared to growing grid and the classical SOM.     

Growing Self-Organizing Map with cross insert for mixed-type data clustering

Self-Organizing Map (SOM) possesses effective capability for visualizing high-dimensional data. Therefore, SOM has numerous applications in visualized clustering. Many growing SOMs have been proposed to overcome the constraint of having a fixed map size in conventional SOMs. However, most growing SOMs lack a robust solution to process mixed-type data which may include numeric, ordinal and categorical values in a dataset. Moreover, the growing scheme has an impact on the quality of resultant maps. In this paper, we propose a Growing Mixed-type SOM (GMixSOM), combining a value representation mechanism distance hierarchy with a novel growing scheme to tackle the problem of analyzing mixed-type data and to improve the quality of the projection map. Experimental results on synthetic and real-world datasets demonstrate that the proposed mechanism is feasible and the growing scheme yields better projection maps than the existing method.     

A hierarchical representation for heterogeneous object modeling

A hierarchical representation for heterogeneous object modeling is presented in this paper. To model a heterogeneous object, Boundary representation is used for geometry representation, and a novel Heterogeneous Feature Tree (HFT) structure is proposed to represent the material distributions. HFT structure hierarchically organizes the material variation dependency relationships and is intuitive in modeling different types of material gradations. Based on the HFT structure, a recursive material evaluation algorithm is proposed to dynamically evaluate the material compositions at a specific location. Such a hierarchical representation guarantees complex material gradations and the user's design intent can be intuitively represented. Example heterogeneous objects modeled with this scheme are provided and potential applications are discussed.     

Visual dynamic model based on self-organizing maps for supervision and fault detection in industrial processes

Visual data mining techniques have experienced a growing interest for processing and interpretation of the large amounts of multidimensional data available in current industrial processes. One of the approaches to visualize data is based on self-organizing maps (SOM), which define a projection of the input space onto a 2D or 3D space that can be used to obtain visual representations. Although these techniques have been usually applied to visualize static relations among the process variables, they have proven to be very useful to display dynamic features of the processes. In this work, an approach based on the SOM to model the dynamics of multivariable processes is presented. The proposed method identifies the process conditions (clusters) and the probabilities of transition among them, using the trajectory followed by the input data on the 2D visualization space. Furthermore, a new method of residual computation for fault detection and identification that uses the dynamic information provided by the model of transitions is proposed. The proposed method for modeling and fault identification has been applied to supervise a real industrial plant and the results are included.     

A hierarchically structured and constraint-based data model for intuitive and precise solid modeling in a virtual reality environment

This article proposes a hierarchically structured and constraint-based data model for intuitive and precise solid modeling in a virtual reality (VR) environment. The data model integrates a high level constraint-based model for intuitive and precise manipulation, a middle level solid model for complete and precise representation and a low-level polygon mesh model for real-time interactions and visualization in a VR environment. The solid model is based on a hybrid B-rep/CSG data structure. Constraints are embedded in the solid model and are organized at hierarchical levels as feature constraints among internal feature elements, part constraints among internal features and assembly constraints between individual parts. In addition to providing a complete and precise model representation and the support for real-time visualization, the proposed data model permits intuitive and precise interaction through constraint-based manipulations for solid modeling in a VR environment. This is a critical issue for product design in a VR environment due to the limited resolutions of today's VR input and output devices.     

Satellite‐based precipitation estimation using watershed segmentation and growing hierarchical self‐organizing map

This paper outlines the development of a multi‐satellite precipitation estimation methodology that draws on techniques from machine learning and morphology to produce high‐resolution, short‐duration rainfall estimates in an automated fashion. First, cloud systems are identified from geostationary infrared imagery using morphology based watershed segmentation algorithm. Second, a novel pattern recognition technique, growing hierarchical self‐organizing map (GHSOM), is used to classify clouds into a number of clusters with hierarchical architecture. Finally, each cloud cluster is associated with co‐registered passive microwave rainfall observations through a cumulative histogram matching approach. The network was initially trained using remotely sensed geostationary infrared satellite imagery and hourly ground‐radar data in lieu of a dense constellation of polar‐orbiting spacecraft such as the proposed global precipitation measurement (GPM) mission. Ground‐radar and gauge rainfall measurements were used to evaluate this technique for both warm (June 2004) and cold seasons (December 2004–February 2005) at various temporal (daily and monthly) and spatial (0.04° and 0.25°) scales. Significant improvements of estimation accuracy are found classifying the clouds into hierarchical sub‐layers rather than a single layer. Furthermore, 2‐year (2003–2004) satellite rainfall estimates generated by the current algorithm were compared with gauge‐corrected Stage IV radar rainfall at various time scales over continental United States. This study demonstrates the usefulness of the watershed segmentation and the GHSOM in satellite‐based rainfall estimations.     

自组织映射算法与基于专家系统的支持向量回归的结合

A new approach is proposed to model nonlinear dynamic systems by combining SOM (self-organizing feature map) with support vector regression (SVR) based on expert system. The whole system has a two-stage neural network architecture. In the first stage SOM is used as a clustering algorithm to partition the whole input space into several disjointed regions. A hierarchical architecture is adopted in the partition to avoid the problem of predetermining the number of partitioned regions. Then, in the second stage, multiple SVR, also called SVR experts, that best fit each partitioned region by the combination of different kernel function of SVR and promote the configuration and tuning of SVR. Finally, to apply this new approach to time-series prediction problems based on the Mackey-Glass differential equation and Santa Fe data, the results show that SVR experts has effective improvement in the generalization performance in comparison with the single SVR model.