Data Mining, Statistical Learning, and Data Visualization for Complex Data

复杂数据的数据挖掘、统计学习和数据可视化

• 批准号: 0532217
• 负责人: William Cleveland
• 金额: --
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-15 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0532217&HistoricalAwards=false
• 关键词: Data; Mining; Statistical; Learning; Visualization

Title: Data Mining, Statistical Learning, and Data Visualization forComplex DataPrincipal Investigator: William S. Cleveland, Shanti S. Gupta Professor ofStatistics and Courtesy Professor of Computer Science, Purdue UniversityThis project studies the building of a computational environment for sixresearch projects that are bound together by the need to model data thatare complex --- large data sets with complex patterns and dependenciesthat require require complex models and algorithms. Three of theprojects have as their goal data analysis: using methods of data mining,statistical learning, and data visualization to study the problems in asubject matter area through the analysis of complex data sets. The otherthree have as their goal new methods: the development of new approachesand tools of data mining, statistical learning, and data visualizationfor complex data. For methods to work best for complex data, they mustbe invented with the challenges of complex data in mind.The environment needed for the analysis of complex data and the inventionof new methods for them is quite different than that of grid computingenvironments, which have received substantial attention in the past. Dataanalysis requires (1) an interactive environment with a good language forprogramming with data that provides rapid prototyping of new methods,and (2) a hardware environment that supports interactive computing with largeamounts of memory and rapid disk access to large databases. A goal of theproject to study how to design such an environment and minimize cost.

标题：复杂数据的数据挖掘、统计学习和数据可视化主要研究者：William S. Cleveland, Shanti S. Gupta统计学教授和普渡大学计算机科学客座教授该项目研究了六个研究项目的计算环境的构建，这些项目由于需要对复杂的数据进行建模而绑定在一起——具有复杂模式和依赖关系的大型数据集，需要复杂的模型和算法。其中三个项目的目标是数据分析：使用数据挖掘、统计学习和数据可视化的方法，通过分析复杂的数据集来研究主题领域的问题。其他三个以新方法为目标：开发数据挖掘、统计学习和复杂数据的数据可视化的新方法和工具。要使方法最适合复杂数据，必须在发明这些方法时考虑到复杂数据的挑战。复杂数据分析所需的环境和新方法的发明与网格计算环境有很大的不同，网格计算环境在过去受到了很大的关注。数据分析需要(1)一个具有良好的数据编程语言的交互式环境，以提供新方法的快速原型，以及(2)一个支持具有大量内存和快速磁盘访问大型数据库的交互式计算的硬件环境。该项目的目标是研究如何设计这样的环境并使成本最小化。