多维数据可视化技术综述

数据挖掘可视化是数据挖掘过程中的重要组成部分。通过交互式数据挖掘可以增强挖掘结果的可理解性和可信度。如何可视化多维数据,是目前可视化研究的热点。本文对目前主要的关于多维数据可视化技术和方法进行了综述。     

在数据挖掘中基于SOM网络的数据分析可视化设计

阐述了SOM（Self-Organizing Map)自组织神经网络和Davies-Bouldin聚类判定法，采用SOM网络构建了数据挖掘中数据模型，设计了SOM网络数据分析可视化软件，并进行了详细的可视化数据分析，同时，设计的软件已经初步应用到数据挖掘当中，取得了良好的效果。     

I-Miner环境下聚类及可视化研究

聚类分析是数据挖掘中的核心技术,利用相关的可视化方法显示聚类结果,将数据分布以直观、形象的图形方式呈现给决策者,使得决策者可以直观地分析数据。I-Miner是一个企业级的数据挖掘工具,利用I-Miner软件进行聚类分析,并用多种方法将聚类结果可视化。通过S语言拓展软件功能,编程实现了K-Medoid算法、SOM算法、SOM与K-Medoids结合的聚类组合算法,尤其是在高维数据的可视化上,实现了星图法和SOM之U矩阵法,弥补软件中聚类和可视化模块较少的不足。     

基于PMML的自组织神经网络元模型

Kohonen自组织特征映射网络SOM因其能够将高维数据映射为二维特征图而广泛应用于数据探索分析活动中。预测模型标记语言标准PMML是一个与平台及系统无关的数据挖掘模型表示语言，但其中并未包含SOM元模型的定义。通过对SOM模型的应用需求分析，提出了基于PMML的SOM元模型定义，可使模型生成与模型存储相分离，使用户在脱离模型生成系统的情况下进行模型的可视化及利用。     

基于共引分析的中草药相互关系可视化算法

从中药方剂中挖掘有用知识是目前中医药信息化的一个重要方向。使用共引分析技术,从大量中药方剂中构建中草药连接关系网络,在此基础上,使用基于链接分析的算法计算中草药的重要度,利用图的链接关系进行图的聚类和自动排列。通过提供多种三维可视化技术,交互式、实时地显示中草药连接关系网络,以使研究人员能够可视化地探索中药方剂及中草药的信息,为数据挖掘提供一种强有力的辅助手段。     

基于ABS算法的可视化金融数据挖掘平台开发

基于GDI+显示技术和图像匹配技术的理论,研究了有关金融数据挖掘的相关技术,在Visual Studio2008编译环境下开发可视化金融数据挖掘平台。为用户提供直观详尽的金融信息,并提供交互式的分析功能。     

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．     

可视化数据挖掘工具的设计与实现

为了在海量数据中把有用的数据提取给用户进行分析,通过对数据可视化和聚类分析的深入研究,将可视化技术与数据挖掘技术两者结合起来,在Java平台下开发一个可视化的数据挖掘系统,把数据挖掘的结果以3D散点图、平行坐标图的方式显示给用户,使用户能够直观地看到数据集的全貌及分析各对象同一属性值的分布和各属性之间的关系,有效地表达数据挖掘结果。     

可视化技术在空间数据挖掘中的应用

在阐述可视化与空间数据挖掘关系的基础上，探讨了可视化在空间数据挖掘过程中应用的各个环节，提出了将具体应用划分为概念层、逻辑层和基础层3个层次。以地质模型数据挖掘为例，对应3个层次阐述了可视化应用的关键技术：地质模型可视化，交互式挖掘与探索性可视化分析。开发了一个原型系统，初步实现了可视化挖掘功能。     

数据场聚类在信息安全中的应用研究

当前信息时代的开放性,使信息安全面临着重大威胁,入侵检测系统成为保证网络安全的一种必要手段,而数据挖掘在网络入侵检测领域是一个重要手段,根据数据场的交互式可视化聚类思想,介绍了利用聚类技术进行网络异常检测的方法.     

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.     

Application of self-organising maps for data mining with incomplete data sets

Self-organising maps (SOM) have become a commonly-used cluster analysis technique in data mining. However, SOM are not able to process incomplete data. To build more capability of data mining for SOM, this study proposes an SOM-based fuzzy map model for data mining with incomplete data sets. Using this model, incomplete data are translated into fuzzy data, and are used to generate fuzzy observations. These fuzzy observations, along with observations without missing values, are then used to train the SOM to generate fuzzy maps. Compared with the standard SOM approach, fuzzy maps generated by the proposed method can provide more information for knowledge discovery.     

基于SOM人工神经网络的网络流量聚类分析

网络性能及使用模式是影响网络应用的关键因素。通过对网络流量的分析，能够反映出网络的使用模式，但网络流量数据中包含大量的冗余信息。通过数据挖掘的方法，能提取出潜在的网络使用模式。使用SOM人工神经网络对局域网流量进行聚类分析，发现同一局域网内的用户，其对网络的使用模式基本相同。同时发现个别不同的网络使用模式，存在少量的使用模式上的“奇点”。     

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.     

Incorporating self-organizing map with text mining techniques for text hierarchy generation

Self-organizing maps (SOM) have been applied on numerous data clustering and visualization tasks and received much attention on their success. One major shortage of classical SOM learning algorithm is the necessity of predefined map topology. Furthermore, hierarchical relationships among data are also difficult to be found. Several approaches have been devised to conquer these deficiencies. In this work, we propose a novel SOM learning algorithm which incorporates several text mining techniques in expanding the map both laterally and hierarchically. On training a set of text documents, the proposed algorithm will first cluster them using classical SOM algorithm. We then identify the topics of each cluster. These topics are then used to evaluate the criteria on expanding the map. The major characteristic of the proposed approach is to combine the learning process with text mining process and makes it suitable for automatic organization of text documents. We applied the algorithm on the Reuters-21578 dataset in text clustering and categorization tasks. Our method outperforms two comparing models in hierarchy quality according to users’ evaluation. It also receives better F1-scores than two other models in text categorization task.     

Pattern discovery: A progressive visual analytic design to support categorical data analysis

When using data-mining tools to analyze big data, users often need tools to support the understanding of individual data attributes and control the analysis progress. This requires the integration of data-mining algorithms with interactive tools to manipulate data and analytical process. This is where visual analytics can help. More than simple visualization of a dataset or some computation results, visual analytics provides users an environment to iteratively explore different inputs or parameters and see the corresponding results. In this research, we explore a design of progressive visual analytics to support the analysis of categorical data with a data-mining algorithm, Apriori. Our study focuses on executing data mining techniques step-by-step and showing intermediate result at every stage to facilitate sense-making. Our design, called Pattern Discovery Tool, targets for a medical dataset. Starting with visualization of data properties and immediate feedback of users’ inputs or adjustments, Pattern Discovery Tool could help users detect interesting patterns and factors effectively and efficiently. Afterward, further analyses such as statistical methods could be conducted to test those possible theories.