博客内容来自悉尼大学(University of Sydney) COMP5048 Visual Analytics 上课笔记，版权所有者Prof. Seokhee Hong
Topics
Approximate schedule: topics are subject to change
Week 1: Introduction
- 可视化分析与信息可视化
- Visual Analytics 可视化分析
- Information Visualization 信息可视化
- Network Visualization (Graph Drawing) 网络可视化
- Aesthetics 审美要求
- Graph Drawing Algorithms 画图算法
- Research Challenges 研究挑战
- Big Complex Data 大型复杂数据
- Visualisation Challenge 可视化挑战
Week 2: Hierarchical Data Visualisation
- Tree Drawing Algorithms 算法
- Terminology
- Layered Drawing
- Radial Drawing
- HV-Drawing
- Other Representation
- Tree Visualisation Methods 方法
- Dendrogram 系统树图（表示亲缘关系的树状图解）
- Hyperbolic Tree Browser 双曲线树
- Space-filling Tree 空间填充式的树
- Treemap
- Icicle Trees (Adjacency Diagram)
- Information Slice and Sunburst Diagrams
- Cone Tree 圆锥体的树
- Polyplane
- Phyllo Tree
- BeamTree 柱状体的树
- Collapsible Cylindrical Trees 可拆解圆柱体的树
- Botanical Tree 植物学的树
Week 3: Visual analytics guest lectures
Week4: Drawing Undirected Graphs: Force Directed Methods用力引导的方法绘制无向图
Force-Directed Methods
Many variations
1. Spring & electrical force
2. Barycenter method
3. Force simulating graph theoretic distance
4. Magnetic field
5. General energy function
6. Constraints
Week 4: Network Data Visualisation
Week 5: Multivariate/Multidimensional Data Visualisation
Week 6: Temporal/Dynamic Data Visualisation
Week 7: Big/Complex Data Visualisation
Week 8: HCI Evaluation Methods
Introduction
Visual Analytics aims to facilitate the data analytics process using Information Visualisation.
Information Visualisation aims to make good pictures of abstract information, such as stock prices, health data, social networks, and software diagrams.
The challenge for Visual Analytics is to design and implement effective Visualisation methods that produce geometric representation of complex data so that data analysts from various domains can visually inspect complex data and carry out critical decision making.
This unit will provide Visualisaiton techniques and fundamental algorithms to achieve good visualisation of abstract information.
可视化分析的目的是通过信息可视化技术突出数据的特征从而进一步分析，目的是分析；
信息可视化的目的是将抽象信息做成容易观察的图形，目的是观察；
可视化分析的主要挑战是找到高效的可视化方法。
这里的内容将会有一些可视化技术和基础算法，希望使用者能有基本的数据结构、算法、编程的基础。
Basic Knowledge in
• Data Structures
• Algorithms
• Programming?