检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Guckes, Kathrin Schäpers, Marc Pohl, Margit Kerren, Andreas von Landesberger, Tatiana Graphical Interactive Systems Group Technical University Darmstadt Germany Informatics TU Wien Austria Visualisierung und Visual Analytics University of Cologne Germany Information Visualization Group Linköping University Sweden

Comparing directed acyclic graphs (DAGs) is essential in various fields such as healthcare, social media, finance, biology, and marketing. DAGs often result from contagion processes over networks, including information spreading, retweet activity, disease transmission, financial crisis propagation, malware spread, and gene mutations. For instance, in disease spreading, an infected patient can transmit the disease to contacts, making it crucial to analyze and predict scenarios. Similarly, in finance, understanding the effects of saving or not saving specific banks during a crisis is vital. Experts often need to identify small differences between DAGs, such as changes in a few nodes or edges. Even the presence or absence of a single edge can be significant. visualization plays a crucial role in facilitating these comparisons. However, standard hierarchical layout algorithms struggle to visualize subtle changes effectively. The typical hierarchical layout, with the root on top, is preferred due to its performance in comparison to other layouts. Nevertheless, these standard algorithms prioritize single-graph aesthetics over comparison suitability, making it challenging for users to spot changes. To address this issue, we propose a layout that enhances shape changes in DAGs while minimizing the impact on aesthetics. Our approach involves outwardly swapping changes, altering the DAG’s shape. We introduce new drawing criteria: 1. Criteria for maximizing outward swaps of graph changes. 2. Criteria for reshaping the DAG by repositioning swapped changes. 3. Criteria for handling changes that cannot be outwardly swapped. Our layout builds upon a Sugiyama-like hierarchical layout and implements these criteria through two extensions. We designed it this way to maintain interchangeability and accommodate future optimizations, such as pseudo-nodes for edge crossing minimization. In our evaluations, our layout achieves excellent results, with edge crossing aesthetics averaging arou

关键词： Finance

来源：评论

学校读者我要写书评

暂无评论

Learn to compress and restore sequential data

Learn to compress and restore sequential data

引用

AAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference

作者： Wang, Yi Feng, Jianhua Liu, Shixia Department of Computer Science Tsinghua University Beijing 100084 China Information Visualization and Interactive Visual Analytics IBM China Research Lab. Beijing 100094 China

No abstract available

ISBN: (纸本)1577353234

No abstract available

关键词：

来源：评论

学校读者我要写书评

暂无评论

Milling naturally smooth evolution of clusters from dynamic data

Milling naturally smooth evolution of clusters from dynamic ...

引用

7th SIAM International Conference on Data Mining

作者： Wang, Yi Liu, Shi-Xia Feng, Jianhua Zhou, Lizhu Department of Computer Science Tsinghua University and IBM China Research Lab. China Information Visualization and Interactive Visual Analytics IBM China Research Lab. China Department of Computer Science Tsinghua University China

ISBN: (纸本)9780898716306

Many clustering algorithms have been proposed to partition a set of static data points into groups. In this paper, we consider an evolutionary clustering problem where the input data points may move, disappeare, and emerge. Generally, these changes should result in a smooth evolution of the clusters. Mining this naturally smooth evolution is valuable for providing an aggregated view of the numerous individual behaviors. We solve this novel and generalized form of clustering problem by converting it into a Bayesian learning problem. Analogous to that the EM clustering algorithm clusters static data points by learning a Gaussian mixture model, our method mines the evolution of clusters from dynamic data points by learning a hidden semi-Markov model (HSMM). By utilizing characteristics of the evolutionary clustering problem, we derive a new unsupervised learning algorithm which is much more efficient than the algorithms used to learn traditional variable-duration HSMMs. Because the HSMM models the probabilistic relationship between the dynamic data set and corresponding evolving clusters, we can interpret the learned parameters as the evolving clusters intuitively using the Viterbi filtering technique. Because learning an HSMM is in fact learning an optimal Viterbi filter, the learned cluster evolutions are smooth and fit well with the data. We demonstrate the effectiveness of this method by experiments on both synthetic data and real data.

关键词： Clustering algorithms

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：