Spectral Analysis for Data Mining

本研究主要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。正在讨论的部分基本问题是：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“重要的”结构和“有意义的”模式？如何以及何时使用这种隐藏的结构来促进确定缺失的数据或“清理”不精确或部分不正确的数据？什么是数据生成的合适模型，如何使用这些模型来改进数据挖掘算法的设计？研究人员正在研究的应用程序已经受到了极大的关注，并在经验上取得了成功，包括对象聚类和网站排名。此外，他们还设计和分析算法，并为新的数据挖掘问题开发和分析模型，包括协同过滤、主题蒸馏、垃圾邮件检测和预防以及分层聚类。矩阵摄动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。通过对实际数据集的实验评估，对理论研究进行了补充。

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.