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A new concentric-circle visualization of multi-dimensional data and its application in network security

JOURNAL OF VISUAL LANGUAGES AND COMPUTING 2010年第4期21卷 194-208页

作者： Lu, Liang Fu Zhang, Jia Wan Huang, Mao Lin Fu, Lei Tianjin Univ Sch Comp Sci & Technol Tianjin 300072 Peoples R China Tianjin Univ Dept Math Tianjin 300072 Peoples R China Univ Technol Sydney Fac Engn & IT Sydney NSW 2007 Australia

With the rapid growth of networked data communications in size and complexity, network administrators today are facing more challenges to protect their networked computers and devices from all kinds of attacks. This paper proposes a new concentric-circle visualization method for visualizing multi-dimensional network data. This method can be used to identify the main features of network attacks, such as DDoS attack, by displaying their recognizable visual patterns. To reduce the edge overlaps and crossings, we arrange multiple axes displayed as concentric circles rather than the traditional parallel lines. In our method, we use polycurves to link values (vertexes) rather than polylines used in parallel coordinate approach. Some heuristics are applied in our new method in order to improve the readability of views. We discuss the advantages as well as the limitations of our new method. In comparison with the parallel coordinate visualization, our approach can reduce more than 15% of the edge overlaps and crossings. In the second stage of the method, we have further enhanced the readability of views by increasing the edge crossing angle. Finally, we introduce our prototype system: a visual interactive network scan detection system called CCScanViewer. It is based on our new visualization approach and the experiments have showed that the new approach is effective in detecting attack features from a variety of networking patterns, such as the features of network scans and DDoS attacks. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Concentric-circle coordinate multi-dimensional data visualization Crossing reduction Network visualization Security visualization Network intrusion detection Polycurve

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Cluster identification with parallel coordinates

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PATTERN RECOGNITION LETTERS 1999年第6期20卷 565-572页

作者： Chou, SY Lin, SW Yeh, CS Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei Taiwan

In this paper, the visualization of multi-dimensional data with the parallel coordinate representation is studied. A particular problem of interest is the identification of lines to which clusters of points are close in an n-dimensional space. By using the duality between the Cartesian and the parallel coordinate systems, a scan-line approach in the parallel coordinate system is established and implemented to aid the identification of such lines. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： parallel coordinates multi-dimensional data visualization duality clustering WWW application

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Visual Analysis of Merchandise Sales Trend Based on Online Transaction Log

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第11期34卷

作者： Yu, Shidong Yang, Dongsheng Hao, Ying Lian, Mengjia Zang, Ying Chinese Acad Sci Shenyang Inst Comp Technol Shenyang 110168 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Yingkou Inst Technol Dept Elect Engn Yingkou 115014 Peoples R China Dalian Maritime Univ Dept Informat Sci Dalian 116026 Peoples R China

Online transaction log records the relevant information of the users, commodities and transactions, as well as changes over time, which can help analysts understand commodities' sales. The existing visualization methods mainly analyze the purchase behavior from the perspective of users, while analyzing the sales trend of commodities can better help merchants to make business decisions. Based on the transaction log, this paper puts forward the visual analysis framework of commodity sales trend and the corresponding data processing algorithm. The concepts of volatility and dynamic performance of sales trend are proposed, through which the multi-dimensional sales data of time-oriented are displayed in two-dimensional space. The "Feature Ring" is designed to display the detailed sales information of the products. Based on the above methods, a visual analysis system is designed and implemented. The usability and validity of the visualization methods are verified by using JD online transaction data. The visualization methods enable manufacturers to formulate production plans and carry out product research and develop better.

关键词： Visual analysis analysis of sales trend multi-dimensional data visualization online transaction log

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visualization of multi-Variate Scientific data

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COMPUTER GRAPHICS FORUM 2009年第6期28卷 1670-1690页

作者： Fuchs, R. Hauser, H. Vienna Univ Technol Inst Comp Graph & Algorithms Vienna Austria VRV Res Ctr Vienna Austria Univ Bergen Dept Informat N-5020 Bergen Norway

In this state-of-the-art report we discuss relevant research works related to the visualization of complex, multi-variate data. We discuss how different techniques take effect at specific stages of the visualization pipeline and how they apply to multi-variate data sets being composed of scalars, vectors and tensors. We also provide a categorization of these techniques with the aim for a better overview of related approaches. Based on this classification we highlight combinable and hybrid approaches and focus on techniques that potentially lead towards new directions in visualization research. In the second part of this paper we take a look at recent techniques that are useful for the visualization of complex data sets either because they are general purpose or because they can be adapted to specific problems.

关键词： visualization scientific visualization multi-variate data visualization multi-dimensional data visualization multi-field visualization scalar data visualization flow visualization tensor visualization illustrative visualization I 3 8 [Computer Graphics]: Computer Graphics Applications

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Painting image browser applying an associate-rule-aware multidimensional data visualization technique

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Visual Computing for Industry,Biomedicine,and Art 2020年第1期3卷 18-30页

作者： Ayaka Kaneko Akiko Komatsu Takayuki Itoh Florence Ying Wang Ochanomizu University 2-1-1 OtsukaBunkyo-ku1128610 TokyoJapan CSIRO Julius Ave.2113 North RydeNSWAustralia

Exploration of artworks is enjoyable but often time *** example,it is not always easy to discover the favorite types of unknown painting *** is not also always easy to explore unpopular painting works which looks similar to painting works created by famous *** paper presents a painting image browser which assists the explorative discovery of user-interested painting *** presented browser applies a new multidimensional data visualization technique that highlights particular ranges of particular numeric values based on association rules to suggest cues to find favorite painting *** study assumes a large number of painting images are provided where categorical information(e.g.,names of artists,created year)is assigned to the *** presented system firstly calculates the feature values of the images as a preprocessing *** the browser visualizes the multidimensional feature values as a heatmap and highlights association rules discovered from the relationships between the feature values and categorical *** mechanism enables users to explore favorite painting images or painting images that look similar to famous painting *** case study and user evaluation demonstrates the effectiveness of the presented image browser.

关键词： Painting image multi-dimensional data visualization Association rule

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Regularized Autoencoder for The Analysis of multivariate Metrology data 36

Regularized Autoencoder for The Analysis of Multivariate Met...

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Conference on Metrology, Inspection, and Process Control XXXVI Part of SPIE Advanced Lithography and Patterning Conference

作者： Saib, Mohamed Lorusso, Gian Francesco Charley, Anne-Laure Leraya, Philippe Kondo, Tsuyoshi Shindo, Hiroyuki Ebizuka, Yasushi Ban, Naoma Ikota, Masami IMEC Celestijnenlaan 1 B-3001 Leuven Belgium Hitachi High Tech Corp Hitachinaka Ibaraki 3128504 Japan

ISBN: (纸本)9781510649828;9781510649811

A new generation of metrology tools has been recently released to the market, allowing extensive characterization of semiconductor samples thanks to massive measurements. The resulting substantial growth of measurement sets in size and number of descriptors made the data analysis with the traditional statistical techniques laborious, being unable to consider all data simultaneously. In this work, we propose an analysis method applicable to the massive measurement sets based on a machine learning technique, called Autoencoder (AE). The performance of the original AE model has been boosted in this work thanks to its regularization, by imposing orthogonality of its internal representation and by training only half of the model weights while the second half is deduced. Practically, once the model is trained on the data set of interest, it is used to study relationships between input variables through the chart of the circle of correlations. In this chart, input variables are projected as vectors into the compression plane where the angular distance between them will express their degree of correlation. The representation of massive measurements data through a condensed simple chart that still shows complex interactions between variables is in fact very efficient and facilitating the interpretation of measurements themselves. The experimental validation of the proposed method has been done with measurements of Contact Hole (CH) patterns acquired on samples manufactured with 3 different exposure conditions (underexposure dose, nominal exposure dose, and overexposure dose). The proposed data analysis technique allowed us to clearly identify the impact of the process conditions on patterns characteristics, such as their eccentricity, their geometry, and more. Our results indicated that in case of overexposure, an anisotropic distortion of the CH geometry is present, where gamma-axis is the major axis, with larger impact on the pattern surface as compared to the X-axis. This

关键词： Regularized autoencoder dimensionality reduction multi-dimensional data visualization Massive metrology SEM inspection Machine Learning

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Constructing and reconstructing the reorderable matrix

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Information visualization 1997年第1期4卷 32-48页

作者： Siirtola, Harri Mäkinen, Erkki Department of Computer Sciences Information Visualization Research Group at TAUCHI University of Tampere Finland

We consider the backgrounds, applications, implementations, and user interfaces of the reorderable matrix originally introduced by Jacques Bertin. As a new tool for handling the matrix, we propose a new kind of interface for interactive cluster analysis. As the main tool to order the rows and columns, we use the well-known barycenter heuristic. Two user tests are performed to verify the usefulness of the automatic tools. © 2005, SAGE Publications. All rights reserved.

关键词： barycenter heuristic cluster analysis multi-dimensional data visualization the reorderable matrix user interface

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Acceleration of EC convergence with landscape visualization and human intervention

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Applied Soft Computing 2002年第4期1卷 245-256页

作者： Hayashida, Norimasa Takagi, Hideyuki Kyushu Institute of Design Minami-ku Fukuoka 815-8540 Japan

We propose Visualized EC/IEC as an evolutionary computation (EC) and interactive EC (IEC) with visualizing individuals in a multi-dimensional searching space in a 2-D space. This visualization helps us envision the landscape of an n-D searching space, so that it is easier for us to join an EC search by indicating the possible global optimum estimated in the 2-D mapped space. We first compare four mapping methods from the points of view of computational time, convergence speed, and visual easiness to grasp whole EC landscape with five benchmark functions and 28 subjects. Then, we choose self-organizing maps for the projection of individuals onto a 2-D space and experimentally evaluate the effect of visualization using a benchmark function. The experimental result shows that the convergence speed of GA with human search on the Visualized space is at least five times faster than a conventional GA. © 2002 Elsevier Science B.V. All rights reserved.

关键词： Accelerating EC convergence Human fatigue Human intervention Interactive evolutionary computation multi-dimensional data visualization Self-organizing maps

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Pixel bar charts: A visualization technique for very large multi-attribute data sets

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Information visualization 2002年第1期1卷 20-34页

作者： Keim, Daniel A. Hao, Ming C. Dayal, Umesh Hsu, Meichun Hewlett-Packard Research Labs Palo Alto California United States CommerceOne Pleasanton California United States AT&T Research Labs Florham Park NJ United States University of Constance Germany

Simple presentation graphics are intuitive and easy-to-use, but show only highly aggregated data presenting only a very small number of data values (as in the case of bar charts) and may have a high degree of overlap occluding a significant portion of the data values (as in the case of the x-y plots). In this article, the authors therefore propose a generalization of traditional bar charts and x-y plots, which allows the visualization of large amounts of data. The basic idea is to use the pixels within the bars to present detailed information of the data records. The so-called pixel bar charts retain the intuitiveness of traditional bar charts while allowing very large data sets to be visualized in an effective way. It is shown that, for an effective pixel placement, a complex optimization problem has to be solved. The authors then present an algorithm which efficiently solves the problem. The application to a number of real-world e-commerce data sets shows the wide applicability and usefulness of this new idea, and a comparison to other well-known visualization techniques (parallel coordinates and spiral techniques) shows a number of clear advantages. © 2002, SAGE Publications. All rights reserved.

关键词： Information visualization multi-dimensional data visualization very large multi-attribute data sets visual data exploration and data mining

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Overcoming the Curse of dimensionality When Clustering multivariate Volume data 13

Overcoming the Curse of Dimensionality When Clustering Multi...

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13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) and International Conference on Information visualization Theory and Applications (IVAPP)

作者： Molchanov, Vladimir Linsen, Lars Jacobs Univ Bremen Germany Westfalische Wilhelms Univ Munster Munster Germany

ISBN: (纸本)9789897583063

Visual analytics of multidimensional data suffer from the curse of dimensionality, i.e., that even large numbers of data points will be scattered in a high-dimensional space. The curse of dimensionality prohibits the proper use of clustering algorithms in the high-dimensional space. Projecting the space before clustering imposes a loss of information and possible mixing of separated clusters. We present an approach where we overcome the curse of dimensionality for a particular type of multidimensional data, namely for attribute spaces of multivariate volume data. For multivariate volume data, it is possible to interpolate between the data points in the high-dimensional attribute space based on their spatial relationship in the volumetric domain (or physical space). We apply this idea to a histogram-based clustering algorithm. We create a uniform partition of the attribute space in multidimensional bins and compute a histogram indicating the number of data samples belonging to each bin. Only non-empty bins are stored for efficiency. Without interpolation, the analysis is highly sensitive to the cell sizes yielding inaccurate clustering for improper choices: Large cells result in no cluster separation, while clusters fall apart for small cells. Using tri-linear interpolation in physical space, we can refine the data by generating additional samples. The refinement scheme can adapt to the data point distribution in attribute space and the histogram's bin size. As a consequence, we can generate a density computation, where clusters stay connected even when using very small cell sizes. We exploit this result to create a robust hierarchical cluster tree. It can be visually explored using coordinated views to physical space visualizations and to parallel coordinates plots. We apply our technique to several datasets and compare the results against results without interpolation.

关键词： multi-dimensional data visualization multi-field data Clustering

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