Spectral Analysis for Data Mining

数据挖掘的频谱分析

• 批准号: 0105406
• 负责人: Anna Karlin
• 金额: $ 22.5万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-15 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0105406&HistoricalAwards=false
• 关键词: Spectral; Analysis; Data; Mining

This research is concerned with the development, analysis and empirical evaluation of algorithms for information retrieval and data mining, primarily using spectral analysis. A partial list of the fundamental questionsbeing addressed are: How should data be stored,organized and processed so as to allow the most effective retrieval ofinformation? How can ``important'' structure and ``meaningful''patterns be found within a large data-set? How and when can thishidden structure be used to facilitate determination of missing dataor to ``clean'' data that is imprecise or partially incorrect?What are appropriate models for data generation, and how canthese models be used to improve the design of data mining algorithms?The researchers are studying applications that have already received a great deal of attention and on whichempirical success has been achieved, including objectclustering and web site ranking. In addition, they are designing and analyzing algorithms, and developing and analyzing models for newer data mining problems, including collaborative filtering, topic distillation, spam detection and prevention, and hierarchicalclustering. Matrix perturbation theory is the foundation for the development of theoretical results. The researchers are also studyingnew techniques for speeding up the computation of the SVD.The theoretical research is being complemented byexperimental evaluation on real data sets.

本研究涉及信息检索和数据挖掘算法的开发、分析和实证评估，主要使用谱分析。正在解决的部分基本问题包括：数据应该如何存储、组织和处理，以便最有效地检索信息？ 如何在大型数据集中找到“重要”结构和“有意义”模式？如何以及何时使用这种隐藏结构来促进丢失数据的确定或“清理”不精确或部分不正确的数据？什么是适合数据生成的模型，以及如何使用这些模型来改进数据挖掘算法的设计研究人员正在研究已经受到广泛关注并取得了经验性成功的应用程序，包括对象聚类和网站排名。此外，他们正在设计和分析算法，并开发和分析新数据挖掘问题的模型，包括协作过滤、主题蒸馏、垃圾邮件检测和预防以及层次聚类。 矩阵微扰理论是理论成果发展的基础。研究人员还在研究加速 SVD 计算的新技术。实际数据集上的实验评估对理论研究进行了补充。