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一个基本问题，构成了探索性数据分析并有助于数据管理的基础。但是，通常存在与获取和分析在现代系统（例如互联网，生物学和社交网络）中常规出现的大规模数据集有关的重要资源和计算成本。在高维数据中发现有意义的集群的能力，这些群集困扰着高噪音，离群值和缺失的观察结果，将对理解这些系统产生重大影响。 该项目旨在开发可靠的聚类方法，这些方法可以通过选择性查询最有用的数据测量值来非常有效地识别簇。光谱聚类是一种流行的技术，它通过分析数据点之间相似性值的矩阵的特征向量来识别簇。该项目研究了丢失和错误数据对特征向量结构的影响，并利用这种理解来开发有效的方法，以智能指导后续数据查询。强大而有效的聚类方法对于识别彼此相互作用的蛋白质和药物组至关重要，为变革性健康技术铺平了道路。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，从而促进了无缝的思想和技术交换。该PI通过与CMU巷计算生物学，发布结果和软件在线（http://wwwwwww.c.cmu.edu/~aarti/~aarti/~aarti/resresearch_projects）的合作，发展和教学跨学科课程以及计算机科学培训的机会（包括计算机科学的人群），参与了计算机科学的机会。