Hierarchical Visualization Techniques for Data Mining

该项目涉及开发交互式可视化和数据管理技术，以探索非常大的多变量数据集。该方法包括扩展目前在现有可视化工具(在WPI开发的XmdvTool)中实施的几种多变量数据可视化技术，以支持数据的分层视图，并支持使用N维画笔聚焦和向下钻取。该项目的阶段如下：(1)识别、设计和实现用于层次划分和/或聚集大型多变量数据集的算法；(2)设计和实现现有多变量可视化技术的扩展版本，以传达所选子树的统计摘要；(3)设计和实现用于管理和查询大型、分层、动态数据集和高效计算层次子树摘要的策略；(4)设计和实现交互工具，以允许通过直接和间接操作进行聚焦、向下钻取、合并和其他探索性操作；(5)使用真实数据集和合成数据集来评估和改进可视化、数据管理和交互式探索工具；以及(6)根据可有效探索的数据的功能性、可用性和局限性来评估系统性能。该项目将导致对多变量数据集的定性挖掘技术的开发，其规模比现有的可视化探索工具大一到两个数量级，为全球变化研究、空间科学和决策支持系统等应用领域提供揭示其数据集中的趋势、模式和异常的技术。Http://www.cs.wpi.edu/~matt/research/XmdvTool/

This project involves the development of interactive visualization and data management techniques for the exploration of very large multivariate data sets. The approach consists of extending several multivariate data visualization techniques currently implemented in an existing visualization tool (XmdvTool, developed at WPI) to support hierarchical views of the data, with support for focusing and drill-down using N-dimensional brushes. The stages of the project are as follows: (1) identify, design, and implement algorithms for hierarchical partitioning and/or clustering large multivariate data sets; (2) design and implement extended versions of existing multivariate visualization techniques to convey statistical summarizations of selected subtrees; (3) design and implement strategies for managing and querying large, hierarchical, dynamic data sets and efficiently computing summarizations of subtrees of the hierarchy; (4) design and implement interactive tools to allow focus, drill-down, consolidation, and other exploratory operations through direct and indirect manipulation; (5) evaluate and refine the visualization, data management, and interactive exploration tools using both real and synthetic data sets; and (6) assess system performance in terms of functionality, usability, and limitations on data which can be effectively explored. This project will result in the development of techniques for the qualitative mining of multivariate data sets one or two orders of magnitude larger than possible with existing visual exploration tools, providing application domains such as global change studies, space science, and decision support systems the technology to uncover trends, patterns, and anomalies within their data sets. http://www.cs.wpi.edu/~matt/research/XmdvTool/