Order, Spacing, and Clustering in Visual Exploration of Large Scale Data

本研究项目的重点是开发可视化、交互和数据管理技术，以解决大规模可视化数据挖掘中的高维和数据类型异构问题。基本方法是跨数据集的维度以及包含标称值或分类值的单个维度应用多分辨率聚类策略，并利用数据轴和数据点的排序和定位来强调数据内部的关系。对于可视化，任务包括开发用于确定数据和维度内部和之间的顺序和可变间距的方法，以及将维度聚类为多分辨率抽象，并将它们集成到几种现有的多变量显示技术中。在交互方面，开发了在多分辨率空间内进行直观导航和探索的工具。这包括交互式重新集群工具，允许用户指导数据对象和维度集群的分割和分组过程。对于数据管理，任务包括开发高维索引和多分辨率数据视图管理，用于高维数据访问，以及支持实时可视化探索的缓存和预取策略。对大型数据集的显示和交互工具的易用性和性能进行了评估。这项研究的结果将为生物信息学、地球和空间科学以及电子商务等领域的数据分析师提供交互式探索日益庞大和复杂的数据集的能力。

The focus of this research project is to develop visualization, interaction, and data management technologies to address the problems of high dimensionality and data type heterogeneity in large-scale visual data mining. The basic approach is to apply multiresolution clustering strategies across the dimensions of a data set as well as within individual dimensions containing nominal or categorical values, and exploit the ordering and positioning of data axes and data points to emphasize relationships within the data. For visualization, the tasks involve the development of methods for determining ordering and variable spacing within and between data and dimensions as well as clustering of dimensions into multi-resolution abstractions, and integrating them into several existing multivariate display techniques. For interaction, tools for intuitive navigation and exploration within the multiresolution spaces are developed. This includes interactive reclustering tools to allow users to guide the process of splitting and grouping clusters of data objects and dimensions. For data management the tasks involve the development of high-dimensional indexing and multi-resolution data view management for high-dimensional data access, and caching and prefetching strategies to support real-time visual exploration. The ease of use as well as performance of the display and interactive tools over large data sets is assessed. The results of this research will provide data analysts in domains such as bioinformatics, earth and space sciences, and e-commerce the ability to interactively explore the increasingly large and complex data sets being generated.