III: Small: Spectral Methods for Active Clustering and Bi-Clustering

III：小：主动聚类和双聚类的谱方法

• 批准号: 1116458
• 负责人: Aarti Singh
• 金额: $ 37.28万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116458&HistoricalAwards=false
• 关键词: III; Small; Spectral; Methods; Active

Clustering or organization of data into groups is a fundamental problem that forms the basis of exploratory data analysis and aids in data management. However, there is often a significant resource and computational cost associated with obtaining and analyzing large-scale datasets that routinely arise in modern systems, such as the Internet, biological and social networks. The ability to discover meaningful clusters in high-dimensional data that is plagued with high noise, outliers and missing observations, will have a significant impact on understanding these systems. This project aims to develop robust clustering methods that can identify clusters very efficiently by selectively querying for the most informative data measurements. Spectral clustering is a popular technique that identifies clusters by analyzing the eigenvectors of a matrix of similarity values between the data points. This project investigates the effect of missing and erroneous data on the eigenvector structure, and leverages this understanding to develop active methods that intelligently guide subsequent data queries. Robust and efficient clustering methods are crucial for identifying groups of proteins and drugs that interact with each other, paving the way for transformative health technologies. These methods are also important for learning and maintaining the organization of computer and social networks, thus promoting seamless exchange of ideas and technology. This PI is involved in disseminating the research through collaborations with the CMU Lane Center for Computational Biology, publishing results and software online (http://www.cs.cmu.edu/~aarti/research_projects), developing and teaching inter-disciplinary courses, as well as the Opportunities for undergraduate women research in Computer Science (OurCS) program at Carnegie Mellon University.

将数据聚类或组织成组是形成探索性数据分析的基础并有助于数据管理的一个基本问题。然而，在互联网、生物和社会网络等现代系统中经常出现的大规模数据集的获取和分析往往需要大量的资源和计算成本。在高维数据中发现有意义的簇的能力将对理解这些系统产生重大影响。高维数据饱受高噪声、离群值和缺失观测的困扰。该项目旨在开发稳健的聚类方法，通过有选择地查询最具信息量的数据度量来非常有效地识别集群。谱聚类是一种流行的技术，它通过分析数据点之间的相似值矩阵的特征向量来识别簇。该项目调查缺失和错误数据对特征向量结构的影响，并利用这一理解来开发智能地指导后续数据查询的主动方法。稳健和高效的聚类方法对于识别相互作用的蛋白质和药物组至关重要，为变革性的卫生技术铺平了道路。这些方法对于学习和维护计算机和社交网络的组织也很重要，从而促进思想和技术的无缝交流。这位PI通过与芝加哥大学莱恩计算生物学中心的合作，在线发布结果和软件，开发和教授跨学科课程，以及卡内基梅隆大学计算机科学本科女性研究项目的机会，参与传播这项研究。