Visual Data Mining Research Articles

国内生态安全研究的知识图谱——基于Citespace的计量分析

PDF HTML阅读 XML下载 导出引用 引用提醒 国内生态安全研究知识图谱——基于Citespace的计量分析 DOI: 10.5846/stxb201211081566 作者: 作者单位: 大连理工大学管理与经济学部,大连理工大学管理与经济学部,大连理工大学管理与经济学部 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金（71320107006，71373035）；中央高校基本科研业务费专项资金资助（DUT13RW419） The knowledge mapping of domestic ecological security research：bibliometric analysis based on citespace Author: Affiliation: Faculty of Management and Economics,Dalian University of Technology,Dalian,Faculty of Management and Economics,Dalian University of Technology,Dalian,Faculty of Management and Economics,Dalian University of Technology,Dalian Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:以CSSCI中2000-2011年299篇生态安全研究论文为研究对象，采用文献共被引网络、关键词共现网络及突现词分析，以信息可视化为手段，对国内生态安全研究现状进行了分析。研究发现，目前国内生态安全研究已完成了基础理论和研究体系的架构，但研究网络过于集中，前沿分支较少。生态安全研究起源、生态安全基础理论、生态风险评价、生态系统评价、土地生态安全研究是国内生态安全研究的主要知识群组。国内生态安全研究主题集聚成3条核心路径：“生态安全基础理论研究”路径、“生态安全-生态系统评价-区域生态安全评价-生态预警”路径、“生态安全-生态风险评价-农村资源生态系统-土地生态安全研究-生态预警”路径。生态预警及土地生态安全研究成为当前生态安全的研究前沿，生态安全研究更趋向于对特色性的生态系统进行分析与评价。 Abstract:Research into ecological security has aroused the widespread attention of scholars. The many differences in ecosystem type, number, spatial distribution and ecological evolutionary process has caused domestic scholars to be continuously innovating. In recent years, alongside enhancements to the basic theory and improvement in research systems, ecological security research has formed different research centers and broken new frontiers. This paper synthesizes information visualization methods, bibliometric methods and data mining algorithms and visualizes the development process and structural relationship of ecological security research using the information visualization analysis software, Citespace II. The research uses data from 299 articles about "ecological security" in the CSSCI database during 2000-2011. Using the co-citation network analysis and burst detection, we analyze the domestic research status of the ecological security research field.The results reveal that domestic research falls into categories of basis theories and system construction: the study of the origin of ecological security; ecological security basic theory; ecological risk assessment; ecological system assessment; and land ecological security, which form the main knowledge groups of domestic ecological security research. The ecological security co-citation network provides visual displays of the evolution path among the knowledge groups of ecological security research. This indicates that ecological security research formed the evolution of knowledge and theory, reflecting the coherence and extension of ecological security research. In short, "the study of the origin of ecological security" is at the edge of the network, showing some independence and is less associated with the follow-up study. "Ecological security basic theory" is in the network center and shows the basis theories of ecological security research. This shows a close correlation with the surrounding; "ecological risk assessment" and "ecological system assessment" which are closely linked to "ecological security basis theory". These are extended theories and the frontier branch of "ecological security basis theory". "Land ecological security" is the frontier branch of "ecological risk assessment". It has developed "ecological risk assessment" research and has provided the practical application of those theories. But the co-citation network also indicates that these research networks tend to be more centralized and have poor extension ability. Domestic ecological security research themes cluster into three core evolution routes: "the basic theoretical research of the ecological security". It indicates that ecological security research has gradually developed the basic theories and expanded ecological security concepts; "ecological security, ecosystem assessment, the regional ecological security assessment, ecological security alarm". It indicates that research topics begin with macro research into common eco-system assessment, which is then refined for the regional eco-system assessment; "ecological security, ecological risk assessment, rural resources ecosystem assessment, land ecological security, ecological security alarm". It indicates that ecological security research has been refined and applied to agro-ecosystems assessment. "Ecological security alert" and "land ecological security" are the current research frontiers. Ecological security research will be more likely to analyze and assess special ecological systems and further strengthen ecosystem scales and functional classification. Ecological security research will tend to create a dynamic, diverse ecosystem and strengthen research in ecosystem evolution and related research in the economy, human activity and ecosystems. 参考文献 相似文献 引证文献

Airport service quality drivers of passenger satisfaction

Purpose – Considering the complexity of the airport industry service palette, it is important to identify which air travel factors are distractors and which factors are enhancers of passenger satisfaction. Building on Herzberg's two-factor motivation theory, this study aims to explore most frequently mentioned attributes of airport service quality and distinguish key drivers for passenger satisfaction/dissatisfaction in the airport context. Design/methodology/approach – A content analysis of 1,095 traveler comments posted between 2010 and 2013 on an airport review web site was performed in order to identify satisfiers/dissatisfiers. The web spider randomly selected consumer comments related to 33 popular destinations. Findings – The study results indicated key satisfiers in the airport context such as cleanliness and pleasant environment to spend time in. On the other side, security-check, confusing signage and poor dining offer are recognized as major dissatisfiers in the airport setting. Practical implications – The study findings provide insight on predominant satisfiers, dissastisfiers and performance factors of airport service quality from passengers' perspectives. Airport management teams may use the study results to renovate airport facility and improve service quality. Originality/value – To the best of the authors' knowledge, this study is the first to utilize the visual data mining techniques in examining airport users' experience. Visualization produced summaries of qualitative comments in the form of tag clouds, word networks, and word tree images that help discover the most emerging themes of travelers' complaints and compliments.

MVClustViz

In the visual data mining, visualization of clusters is a challenging task. Although lots of techniques already have been developed, the challenges still remain to represent large volume of data with multiple dimension and overlapped clusters. In this paper, a multivariate clusters visualization technique (MVClustViz) has been presented to visualize the centroid-based clusters. The geographic projection technique supports multi-dimension, large volume, and both crisp and fuzzy clusters visualization. This technique is most suitable for range analysis of defense related data.

A Novel Visual Data Mining Module for the Geographical Information System gvSIG

The exploration of large GIS models containing spatio-temporal information is a challenge. In this paper we propose the integration of scientific visualization (ScVis) techniques into geographic information systems (GIS) as an alternative for the visual analysis of data. Providing GIS with such tools improves the analysis and understanding of datasets with very low spatial density and allows to find correlations between variables in time and space. In this regard, we present a new visual data mining tool for the GIS gvSIG. This tool has been implemented as a gvSIG module and contains several ScVis techniques for multiparameter data with a wide range of possibilities to explore interactively the data. The developed module is a powerful visual data mining and data visualization tool to obtain knowledge from multiple datasets in time and space. A real case study with meteorological data from Villa Clara province (Cuba) is presented, where the implemented visualization techniques were used to analyze the available datasets. Although it is tested with meteorological data, the developed module is of general application in the sense that it can be used in multiple application fields related with Earth Sciences.

Visual data mining with self-organising maps for ventricular fibrillation analysis

Detection of ventricular fibrillation (VF) at an early stage is being deeply studied in order to lower the risk of sudden death and allows the specialist to have greater reaction time to give the patient a good recovering therapy. Some works are focusing on detecting VF based on numerical analysis of time–frequency distributions, but in general the methods used do not provide insight into the problem. However, this study proposes a new methodology in order to obtain information about this problem. This work uses a supervised self-organising map (SOM) to obtain visually information among four important groups of patients: VF (ventricular fibrillation), VT (ventricular tachycardia), HP (healthy patients) and AHR (other anomalous heart rates and noise). A total number of 27 variables were obtained from continuous surface ECG recordings in standard databases (MIT and AHA), providing information in the time, frequency, and time–frequency domains. self-organising maps (SOMs), trained with 11 of the 27 variables, were used to extract knowledge about the variable values for each group of patients. Results show that the SOM technique allows to determine the profile of each group of patients, assisting in gaining a deeper understanding of this clinical problem. Additionally, information about the most relevant variables is given by the SOM analysis.

Improving seabed classification from Multi-Beam Echo Sounder (MBES) backscatter data with visual data mining

Multi-Beam Echo Sounders are often used for classification of seabed type, as there exists a strong link between sonar backscatter and sediment characteristics of the seabed. Most of the methods for seabed classification from MBES backscatter create a highly-dimensional data set of statistical features and then use a combination of Principal Component Analysis and k-means clustering to derive classes. This procedure can be time consuming for contemporary large MBES data sets with millions of records. This paper examines the complexity of one of most commonly used classification approaches and suggests an alternative where feature data set is optimised in terms of dimensionality using computational and visual data mining. Both the original and the optimised method are tested on an MBES backscatter data set and validated against ground truth. The study found that the optimised method improves accuracy of classification and reduced complexity of processing. This is an encouraging result, which shows that bringing together methods from acoustic classification, visual data mining, spatial analysis and remote sensing can support the unprecedented increases in data volumes collected by contemporary acoustic sensors.

Visual Data Mining of Biological Networks: One Size Does Not Fit All

High-throughput technologies produce massive amounts of data. However, individual methods yield data specific to the technique used and biological setup. The integration of such diverse data is necessary for the qualitative analysis of information relevant to hypotheses or discoveries. It is often useful to integrate these datasets using pathways and protein interaction networks to get a broader view of the experiment. The resulting network needs to be able to focus on either the large-scale picture or on the more detailed small-scale subsets, depending on the research question and goals. In this tutorial, we illustrate a workflow useful to integrate, analyze, and visualize data from different sources, and highlight important features of tools to support such analyses.

The Research of Visualization Data Mining in Industrial Data

Based on characteristics of data distribution in the low dimension nature, this paper studies the effect of manifold embedding technology used in typical industrial data set analysis, and manifold learning theory was introduced into the visualization data mining to help get quick effective information in a large number of data. Cognitive process with the help of that information reduces the difficulty of analysis and improves the data utilization efficiency, having a high value in engineering application.

ENCODE data in the UCSC Genome Browser: year 5 update.

The Encyclopedia of DNA Elements (ENCODE), http://encodeproject.org, has completed its fifth year of scientific collaboration to create a comprehensive catalog of functional elements in the human genome, and its third year of investigations in the mouse genome. Since the last report in this journal, the ENCODE human data repertoire has grown by 898 new experiments (totaling 2886), accompanied by a major integrative analysis. In the mouse genome, results from 404 new experiments became available this year, increasing the total to 583, collected during the course of the project. The University of California, Santa Cruz, makes this data available on the public Genome Browser http://genome.ucsc.edu for visual browsing and data mining. Download of raw and processed data files are all supported. The ENCODE portal provides specialized tools and information about the ENCODE data sets.

Performance Analysis for Visual Data Mining Classification Techniques of Decision Tree, Ensemble and SOM

This research paper is a comprehensive report on experimental setup, data collection methods, implementation and result analyses of market segmentation and forecasting using neural network based artificial intelligence tools. The main focus of the paper is on visual data mining applications for enhancing business decisions. The software based system is implemented as a fully automated and intelligent enough to take into effect of each sales transaction. It updates and instantly modifies forecasting statistics by receiving input sales data directly from sales counter through networked connectivity. The connectivity may be wired or wireless. Three artificial intelligence tools, namely decision tree, ensemble classifier and Self Organizing Maps (SOM) are used for data processing and data analysis. The visual data mining concept is implemented by presenting results in the form of visual interpretation in as simple as possible way to understand very complex statistics. The current research results are mapped to interactive visualization by using multilevel pie charts, multi bar charts, histograms, scatter plots, tree maps and dataflow diagrams. The different visualization techniques help in understanding different levels of information hidden in very large data sets. The results analysis show that decision tree has classified data correctly up to a 86.0 %, ensemble techniques produced an average of 88.0 % and the predictions using SOM has accuracy of 90.0 %. The survey carried out after implementation and use of the system shows that the system is very easy to understand and can be interpreted quickly with minimum efforts. General Terms Computer science and engineering, information technology, data mining, market, business.

A comprehensive preference-based optimization framework with application to high-lift aerodynamic design

An integral component of transport aircraft design is the high-lift configuration, which can provide significant benefits in aircraft payload-carrying capacity. However, aerodynamic optimization of a high-lift configuration is a computationally challenging undertaking, due to the complex flow-field. The use of a designer-interactive multiobjective optimization framework is proposed, which identifies and exploits preferred regions of the Pareto frontier. Visual data mining tools are introduced to statistically extract information from the design space and confirm the relative influence of both variables and objectives to the preferred interests of the designer. The framework is assisted by the construction of time-adaptive Kriging models, which are cooperatively used with a high-fidelity Reynolds-averaged Navier–Stokes solver. The successful integration of these design tools is facilitated through the specification of a reference point, which can ideally be based on an existing design configuration. The framework is demonstrated to perform efficiently for the present case-study within the imposed computational budget.

Data and knowledge visualization with virtual reality spaces, neural networks and rough sets: Application to cancer and geophysical prospecting data

Visual data mining with virtual reality spaces is used for the representation of data and symbolic knowledge. High quality structure-preserving and maximally discriminative visual representations can be obtained using a combination of neural networks (SAMANN and NDA) and rough sets techniques, so that a proper subsequent analysis can be made. The approach is illustrated with two types of data: for gene expression cancer data, an improvement in classification performance with respect to the original spaces was obtained; for geophysical prospecting data for cave detection, a cavity was successfully predicted.

Detection and summarization of genuine review using Visual Data Mining

In earlier days we were asking our friends or relatives for their opinions regarding products which we want to purchase from the merchants. But now a day’s E-commerce is gaining more and more popularity. Whatever query we are having, we can find its answer from World Wide Web. Merchants are also selling their products online and at a same time they are asking customer’s review regarding products, which customer has bought. This would be beneficial to merchants as well as customers also. As the numbers of customers are growing, reviews received by products are also growing in large amount. Thus, mining opinions from product reviews is an important research topic. However, existing research is more focused towards classification and summarization of such online opinions. An important issue related to the trustworthiness of online opinions has been neglected most often. There is no reported study on assessing the trustworthiness of reviews. This research paper aims to first classify the opinion (positive or negative) carried out by detection of a review( spam or a nonspam ) based on rating behavior and finally removing spam reviews, which provides a trusted review to help the customer in taking appropriate buying decision. This paper proposes a novel and effective technique, which will represent classified opinion in form of “chernoff face”. General Terms Opinion mining, Visual data mining

Exploratory analysis of the interrelations between co-located boolean spatial features using network graphs

Visual data mining of spatial data is a challenging task. As exploratory analysis is fundamental, it is beneficial to explore the data using different potential visualisations. In this article, we propose and analyse network graphs as a useful visualisation tool to mine spatial data. Due to their ability to represent complex systems of relationships in a visually insightful and intuitive way, network graphs offer a rich structure that has been recognised in many fields as a powerful visual representation. However, they have not been sufficiently exploited in spatial data mining, where they have principally been used on data that come with an explicit pre-specified network graph structure. This research presents a methodology with which to infer relationship network graphs for large collections of boolean spatial features. The methodology consists of four principal stages: (1) define a co-location model, (2) select the type of co-association of interest, (3) compute statistical diagnostics for these co-associations and (4) construct and visualise a network graph of the statistic from step (3). We illustrate the potential usefulness of the methodology using an example taken from an ecological setting. Specifically, we use network graphs to understand and analyse the potential interactions between potential vector and reservoir species that enable the propagation of leishmaniasis, a disease transmitted by the bite of sandflies.

The application of a visual data mining framework to determine soil, climate and land use relationships

In this research study, the methodology of action research dynamics and a case study was employed in constructing a visual data mining framework for the processing and analysis of geographic land-use data in an agricultural context. The geographic data was made up of a digital elevation model (DEM), soil and land use profiles that were juxtaposed with previously captured climatic data from fixed weather stations in Australia. In this pilot study, monthly rainfall profiles for a selected study area were used to identify areas of soil variability. The rainfall was sampled for the beginning (April) of the rainy season for the known ‘drought’ year 2002 for the South West of Western Australia. The components of the processing framework were a set of software tools such as ArcGis, QuantumGIS and the Microsoft Access database as part of the pre-processing layer. In addition, the GRASS software package was used for producing the map overlays. Evaluation was carried out using techniques of visual data mining to detect the patterns of soil types found for the cropping land use. This was supported by analysis using WEKA and Microsoft Excel for validation. The results suggest that agriculture in these areas of high soil variability need to be managed differently to the more consistent cropping areas. Although this processing framework was used to analyse soil and rainfall climate data pertaining to agriculture in Western Australia; it is easily applicable to other datasets of a similar attribution in different areas.

Improved Similarity Trees and their Application to Visual Data Classification

An alternative form to multidimensional projections for the visual analysis of data represented in multidimensional spaces is the deployment of similarity trees, such as Neighbor Joining trees. They organize data objects on the visual plane emphasizing their levels of similarity with high capability of detecting and separating groups and subgroups of objects. Besides this similarity-based hierarchical data organization, some of their advantages include the ability to decrease point clutter; high precision; and a consistent view of the data set during focusing, offering a very intuitive way to view the general structure of the data set as well as to drill down to groups and subgroups of interest. Disadvantages of similarity trees based on neighbor joining strategies include their computational cost and the presence of virtual nodes that utilize too much of the visual space. This paper presents a highly improved version of the similarity tree technique. The improvements in the technique are given by two procedures. The first is a strategy that replaces virtual nodes by promoting real leaf nodes to their place, saving large portions of space in the display and maintaining the expressiveness and precision of the technique. The second improvement is an implementation that significantly accelerates the algorithm, impacting its use for larger data sets. We also illustrate the applicability of the technique in visual data mining, showing its advantages to support visual classification of data sets, with special attention to the case of image classification. We demonstrate the capabilities of the tree for analysis and iterative manipulation and employ those capabilities to support evolving to a satisfactory data organization and classification.

NAViGaTOR: Large Scalable and Interactive Navigation and Analysis of Large Graphs

Network visualization tools offer features enabling a variety of analyses to satisfy diverse requirements. Considering complexity and diversity of data and tasks, there is no single best layout, no single best file format or visualization tool: one size does not fit all. One way to cope with these dynamics is to support multiple scenarios and workflows. NAViGaTOR (Network Analysis, Visualization & Graphing TORonto) offers a complete system to manage diverse workflows from one application. It allows users to manipulate large graphs interactively using an innovative graphical user interface (GUI) and through fast layout algorithms with a small memory footprint. NAViGaTOR facilitates integrative network analysis by supporting not only visualization but also visual data mining.

Erratum to: Implementation of visual data mining for unsteady blood flow field in an aortic aneurysm

Erratum to: Implementation of visual data mining for unsteady blood flow field in an aortic aneurysm

ENCODE whole-genome data in the UCSC Genome Browser: update 2012.

The Encyclopedia of DNA Elements (ENCODE) Consortium is entering its 5th year of production-level effort generating high-quality whole-genome functional annotations of the human genome. The past year has brought the ENCODE compendium of functional elements to critical mass, with a diverse set of 27 biochemical assays now covering 200 distinct human cell types. Within the mouse genome, which has been under study by ENCODE groups for the past 2 years, 37 cell types have been assayed. Over 2000 individual experiments have been completed and submitted to the Data Coordination Center for public use. UCSC makes this data available on the quality-reviewed public Genome Browser (http://genome.ucsc.edu) and on an early-access Preview Browser (http://genome-preview.ucsc.edu). Visual browsing, data mining and download of raw and processed data files are all supported. An ENCODE portal (http://encodeproject.org) provides specialized tools and information about the ENCODE data sets.

Visual Data Mining: An Exploratory Approach to Analyzing Temporal Patterns of Eye Movements.

Infant eye movements are an important behavioral resource to understand early human development and learning. But the complexity and amount of gaze data recorded from state-of-the-art eye-tracking systems also pose a challenge: how does one make sense of such dense data? Toward this goal, this article describes an interactive approach based on integrating top-down domain knowledge with bottom-up information visualization and visual data mining. The key idea behind this method is to leverage the computational power of the human visual system. Thus, we propose an approach in which scientists iteratively examine and identify underlying patterns through data visualization and link those discovered patterns with top-down knowledge/hypotheses. Combining bottom-up data visualization with top-down human theoretical knowledge through visual data mining is an effective and efficient way to make discoveries from gaze data. We first provide an overview of the underlying principles of this new approach of human-in-the-loop knowledge discovery and then show several examples illustrating how this interactive exploratory approach can lead to new findings.