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

将数据聚类或组织成组是一个基本问题，它构成了探索性数据分析的基础，并有助于数据管理。然而，获取和分析现代系统（如互联网、生物和社会网络）中经常出现的大规模数据集，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集群的能力，这些数据受到高噪声、异常值和缺失观测的困扰，将对理解这些系统产生重大影响。该项目旨在开发健壮的聚类方法，可以通过有选择地查询最具信息量的数据测量来非常有效地识别聚类。谱聚类是一种流行的技术，它通过分析数据点之间相似值矩阵的特征向量来识别聚类。该项目研究了缺失和错误数据对特征向量结构的影响，并利用这种理解开发智能指导后续数据查询的主动方法。强大而有效的聚类方法对于确定相互作用的蛋白质和药物组至关重要，从而为变革性卫生技术铺平道路。这些方法对于学习和维护计算机和社会网络的组织也很重要，从而促进思想和技术的无缝交流。该PI参与通过与CMU Lane计算生物学中心合作传播研究成果，在线发布结果和软件（http://www.cs.cmu.edu/~aarti/research_projects），开发和教授跨学科课程，以及卡内基梅隆大学计算机科学（OurCS）项目的本科女性研究机会。

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.