检索结果-内蒙古大学图书馆

22nd International conference Information visualisation (iv) - Biomedical visualization, visualisation on Built and Rural Environments and Geometric Modelling and Imaging

作者： Wang, Wenbo Li, Yuwei Wang, Feng Liu, Xiaopei Zheng, Youyi ShanghaiTech Univ Shanghai Peoples R China Jilin Univ Changchun Jilin Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538672020

The exploration and analysis of data mining methodologies is an important task for effective knowledge discovery, especially in today's heterogeneous information networks. Previously presented approaches for mining optimization aim primarily at the improvements of time complexity, space complexity, accuracy, and robustness. We extend the state-of-the-art method by concentrating on user-availability and algorithm understandability. Specifically, we use Rankclus, a classic clustering algorithm as an example. After uncovering the unseen computing processes to be displayed in a visual form, the whole clustering processes are transparent to the users, which may help them more clearly and quickly understand how the algorithms are computed, how does each object influence one another. In addition, we use a density approach to intuitively simplify the discovery of data patterns, and through the visualized results, users can adjust algorithm parameters with or without professional training. Finally, we use another two visual techniques to improve the visualization quality: a heatmap matrix designed for checking the similarities of objects which are in the same cluster, and a DOItree implemented to further analyze the accuracy of the algorithms.

关键词： data mining heterogeneous information networks visualization Rankclus

来源：评论

学校读者我要写书评

暂无评论

TransGraph: Hierarchical exploration of Transition Relationships in Time-Varying Volumetric data

引用

IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2011年第12期17卷 2015-2024页

作者： Gu, Yi Wang, Chaoli Michigan Technol Univ Dept Comp Sci Houghton MI 49931 USA

A fundamental challenge for time-varying volume data analysis and visualization is the lack of capability to observe and track data change or evolution in an occlusion-free, controllable, and adaptive fashion. In this paper, we propose to organize a time-varying data set into a hierarchy of states. By deriving transition probabilities among states, we construct a global map that captures the essential transition relationships in the time-varying data. We introduce the TransGraph, a graph-based representation to visualize hierarchical state transition relationships. The TransGraph not only provides a visual mapping that abstracts data evolution over time in different levels of detail, but also serves as a navigation tool that guides data exploration and tracking. The user interacts with the TransGraph and makes connection to the volumetric data through brushing and linking. A set of intuitive queries is provided to enable knowledge extraction from time-varying data. We test our approach with time-varying data sets of different characteristics and the results show that the TransGraph can effectively augment our ability in understanding time-varying data.

关键词： Time-varying data visualization hierarchical representation states transition relationship user interface

来源：评论

学校读者我要写书评

暂无评论

Sequence Synopsis: Optimize visual Summary of Temporal Event data

引用

IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2018年第1期24卷 45-55页

作者： Chen, Yuanzhe Xu, Panpan Ren, Liu Hong Kong Univ Sci & Technol Hong Kong Hong Kong Peoples R China Bosch Res North Amer Palo Alto CA USA

Event sequences analysis plays an important role in many application domains such as customer behavior analysis, electronic health record analysis and vehicle fault diagnosis. Real-world event sequence data is often noisy and complex with high event cardinality, making it a challenging task to construct concise yet comprehensive overviews for such data. In this paper. we propose a novel visualization technique based on the minimum description length (MDL) principle to construct a coarse-level overview of event sequence data while balancing the information loss in it. The method addresses a fundamental trade-off in visualization design: reducing visual clutter vs. increasing the information content in a visualization. The method enables simultaneous sequence clustering and pattern extraction and is highly tolerant to noises such as missing or additional events in the data. Based on this approach we propose a visual analytics framework with multiple levels-of-detail to facilitate interactive data exploration. We demonstrate the usability and effectiveness of our approach through case studies with two real-world datasets. One dataset showcases a new application domain for event sequence visualization, i.e., fault development path analysis in vehicles for predictive maintenance. We also discuss the strengths and limitations of the proposed method based on user feedback.

关键词： Time Series data data Transformation and Representation visual Knowledge Representation visual Analytics

来源：评论

学校读者我要写书评

暂无评论

Information visualisation: AI and visual Analytics and data Science, iv 2021

Information Visualisation: AI and Visual Analytics and Data ...

引用

25th International conference Information visualisation, iv 2021

ISBN: (纸本)9781665438278

The proceedings contain 50 papers. The topics discussed include: visualization of sub-network sets by iterative graph sampling from large scale networks;multidimensional data visualization for investigation of skin transparency;towards a visual approach for representing analytical provenance in exploration processes;automatic creation of a vowel dataset for performing prosody analysis in ASD screening;visual analytics to support industrial vehicle fleet planning;visualization tool to support fraud detection;real-time visualization reconstruction in a real-world environment using augmented reality;visually exploring a collaborative augmented reality taxonomy;a brief review of dashboard visualizations employed to support management or business decisions;and visual exploration of the inner representation learned by a convolutional neural network.

关键词：

来源：评论

学校读者我要写书评

暂无评论

visual Analytic System for Subject Matter Expert Document Tagging using Information Retrieval and Semi-Supervised Machine Learning 23

Visual Analytic System for Subject Matter Expert Document Ta...

引用

23rd International conference on the Information visualisation (iv) - Incorporating Biomedical visualization, Learning Analytics and Geometric Modelling and Imaging

作者： Hagerman, Craig Brath, Richard Langevin, Scott Uncharted Software Toronto ON Canada

ISBN: (纸本)9781728128382

We present a system that combines ambient visualization, information retrieval and machine learning to facilitate the ease and quality of document classification by subject matter experts for the purpose of organizing documents by "tags" inferred by the resultant classifiers. This system includes data collection, a language model, query exploration, feature selection, semi-supervised machine learning and a visual analytic workflow enabling non-data scientists to rapidly define, verify, and refine high-quality document classifiers.

关键词： visual analytics classifiers machine learning ambient visualization mixed-initiative analytics information retrieval

来源：评论

学校读者我要写书评

暂无评论

Multi-Granular Trend Detection for Time-Series analysis

引用

IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2017年第1期23卷 661-670页

作者： van Goethem, Arthur Staals, Frank Loffler, Maarten Dykes, Jason Speckmann, Bettina TU Eindhoven Eindhoven Netherlands Aarhus Univ MADALGO DK-8000 Aarhus C Denmark Univ Utrecht NL-3508 TC Utrecht Netherlands City Univ London London England

Time series (such as stock prices) and ensembles (such as model runs for weather forecasts) are two important types of one-dimensional time-varying data. Such data is readily available in large quantities but visual analysis of the raw data quickly becomes infeasible, even for moderately sized data sets. Trend detection is an effective way to simplify time-varying data and to summarize salient information for visual display and interactive analysis. We propose a geometric model for trend-detection in one-dimensional time-varying data, inspired by topological grouping structures for moving objects in two-or higher-dimensional space. Our model gives provable guarantees on the trends detected and uses three natural parameters: granularity, support-size, and duration. These parameters can be changed on-demand. Our system also supports a variety of selection brushes and a time-sweep to facilitate refined searches and interactive visualization of (sub-)trends. We explore different visual styles and interactions through which trends, their persistence, and evolution can be explored.

关键词： Interactive exploration Trend Detection Time Series

来源：评论

学校读者我要写书评

暂无评论

Big data visualisation and visual Analytics for Music data Mining 22

Big Data Visualisation and Visual Analytics for Music Data M...

引用

22nd International conference Information visualisation (iv) - Biomedical visualization, visualisation on Built and Rural Environments and Geometric Modelling and Imaging

作者： Barkwell, Katrina E. Cuzzocrea, Alfredo Leung, Carson K. Ocran, Ashley A. Sanderson, Jennifer M. Stewart, James Ayrton Wodi, Bryan H. Univ Manitoba Dept Comp Sci Winnipeg MB Canada Univ Trieste Dept Engn & Architecture DIA Trieste TS Italy

ISBN: (纸本)9781538672020

As high volumes of a wide variety of valuable data of different veracities can be easily generated or collected at a high velocity nowadays, big data visualisation and visual analytics are in demand in various real-life applications. Musical data are examples of big data. Embedded in these big data are useful information and valuable knowledge. Many existing big data mining algorithms return useful information and valuable knowledge in textual or tabular forms. Knowing that "a picture is worth a thousand words", big data visualisation and visual analytics are also in demand. In this paper, we present a system for visualising and analysing big data. In particular, our system focuses on the big data science task of the discovery and exploration of frequent patterns (i.e., collections of items that frequently occurring together) from musical data. Evaluation results show the applicability of our system in big data visualisation and visual analytics for music data mining.

关键词： big data data visualisation visual analytics visualiser frequent patterns musical data music data analytics music data mining

来源：评论

学校读者我要写书评

暂无评论

Extracting Events from Spatial Time Series

Extracting Events from Spatial Time Series

引用

14th International conference on Information visualisation (iv)

作者： Andrienko, Gennady Andrienko, Natalia Mladenov, Martin Mock, Michael Poelitz, Christian Fraunhofer IAIS St Augustin Germany Univ Bonn Bonn Germany

ISBN: (纸本)9780769541655

C An important task in exploration of data about phenomena and processes that develop over time is detection of significant changes that happened to the studied phenomenon. Our research is focused on supporting detection of significant changes, called events, in multiple time series of numeric values. We developed a suite of visual analytics techniques that combines interactive visualizations on time-aware displays and maps with statistical event detection methods implemented in R. We demonstrate the utility of our approach using two large data sets.

关键词： visual analytics event detection time series

来源：评论

学校读者我要写书评

暂无评论

visual exploration of Nasal Airflow

引用

IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2009年第6期15卷 1407-1414页

作者： Zachow, Stefan Muigg, Philipp Hildebrandt, Thomas Doleisch, Helmut Hege, Hans-Christian ZIB Berlin Germany Vienna Univ Technol Inst Comp Graph & Algorithms Vienna Austria

Rhinologists are often faced with the challenge of assessing nasal breathing from a functional point of view to derive effective therapeutic interventions. While the complex nasal anatomy can be revealed by visual inspection and medical imaging, only vague information is available regarding the nasal airflow itself: Rhinomanometry delivers rather unspecific integral information on the pressure gradient as well as on total flow and nasal flow resistance. In this article we demonstrate how the understanding of physiological nasal breathing can be improved by simulating and visually analyzing nasal airflow, based on an anatomically correct model of the upper human respiratory tract. In particular we demonstrate how various Information visualization (InfoVis) techniques, such as a highly scalable implementation of parallel coordinates, time series visualizations, as well as unstructured grid multi-volume rendering, all integrated within a multiple linked views framework, can be utilized to gain a deeper understanding of nasal breathing. Evaluation is accomplished by visual exploration of spatio-temporal airflow characteristics that include not only information on flow features but also on accompanying quantities such as temperature and humidity. To our knowledge, this is the first in-depth visual exploration of the physiological function of the nose over several simulated breathing cycles under consideration of a complete model of the nasal airways, realistic boundary conditions, and all physically relevant time-varying quantities.

关键词： Flow visualization exploratory data analysis interactive visual analysis of scientific data time-dependent data

来源：评论

学校读者我要写书评

暂无评论

Mobility Graphs: visual analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering

引用

IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2016年第1期22卷 11-20页

作者： von Landesberger, Tatiana Brodkorb, Felix Roskosch, Philipp Andrienko, Natalia Andrienko, Gennady Kerren, Andreas Tech Univ Darmstadt Darmstadt Germany Fraunhofer IAIS Bonn Germany City Univ London London EC1V 0HB England Linnaeus Univ Vaxjo Sweden

Learning more about people mobility is an important task for official decision makers and urban planners. Mobility data sets characterize the variation of the presence of people in different places over time as well as movements (or flows) of people between the places. The analysis of mobility data is challenging due to the need to analyze and compare spatial situations (i.e., presence and flows of people at certain time moments) and to gain an understanding of the spatio-temporal changes (variations of situations over time). Traditional flow visualizations usually fail due to massive clutter. Modern approaches offer limited support for investigating the complex variation of the movements over longer time periods. We propose a visual analytics methodology that solves these issues by combined spatial and temporal simplifications. We have developed a graph-based method, called Mobility Graphs, which reveals movement patterns that were occluded in flow maps. Our method enables the visual representation of the spatio-temporal variation of movements for long time series of spatial situations originally containing a large number of intersecting flows. The interactive system supports data exploration from various perspectives and at various levels of detail by interactive setting of clustering parameters. The feasibility our approach was tested on aggregated mobility data derived from a set of geolocated Twitter posts within the Greater London city area and mobile phone call data records in Abidjan, ivory Coast. We could show that Mobility Graphs support the identification of regular daily and weekly movement patterns of resident population.

关键词： visual analytics movement data networks graphs temporal aggregation spatial aggregation flows clustering

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：