Hierarchical Visualization Techniques for Data Mining

数据挖掘的层次可视化技术

• 批准号: 9732897
• 负责人: Matthew Ward
• 金额: $ 29.98万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-01 至 2002-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9732897&HistoricalAwards=false
• 关键词: Hierarchical; Visualization; Techniques; Data; Mining

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/

该项目涉及开发交互式可视化和数据管理技术，用于探索非常大的多元数据集。该方法包括扩展当前在现有可视化工具（XmdvTool，由WPI开发）中实现的几种多元数据可视化技术，以支持数据的分层视图，并支持使用n维笔刷进行聚焦和钻取。项目的阶段如下：(1)识别、设计和实现用于分层划分和/或聚类大型多元数据集的算法；(2)设计和实现现有多元可视化技术的扩展版本，以传达所选子树的统计汇总；(3)设计和实现管理和查询大型、分层、动态数据集的策略，并有效地计算分层子树的汇总；(4)设计和实施交互式工具，通过直接和间接的操作进行集中、下钻、整合和其他探索性操作；(5)使用真实和合成数据集评估和完善可视化、数据管理和交互式探索工具；(6)从功能、可用性和可有效探索的数据限制方面评估系统性能。该项目将导致多变量数据集定性挖掘技术的发展，比现有的可视化勘探工具可能大一到两个数量级，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数据集中趋势、模式和异常的技术。http://www.cs.wpi.edu/~matt/research/XmdvTool/