CLUSTERING AND SEARCH FOR INFORMATION SYSTEMS USED BY BIOLOGISTS

生物学家使用的信息系统的聚类和搜索

• 批准号: 8171765
• 负责人: WILLIAM W COHEN
• 金额: $ 0.11万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8171765
• 关键词: Affinity; Codon Nucleotides; Computer Retrieval of Information on Scientific Projects Database; Data; Funding; Grant; Information Systems; Institution; Methods; Research; Research Personnel; Resources; Source; Techniques; United States National Institutes of Health; base; computing resources; parallel processing

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Spectral clustering is an effective and elegant clustering method based on the pairwise similarity between objects. Recently I have developed a fast and simple spectral-clustering like technique called power iteration clustering. As in spectral clustering, points are embedded in a low-dimensional subspace derived from the similarity matrix for the data points; however, while in spectral clustering, the subspace is derived from the bottom eigenvectors of the Laplacian of an affinity matrix, in our proposed method, the subspace is an approximation to a linear combination of these eigenvectors. The new method obtains comparable or better clusters than existing spectral methods, but is extremely scalable, and well-suited to parallel processing on a cluster machine (such as codon or warhol). I would like to explore use of this clustering method for information spaces associated with biologists( personal information needs; this is a project funded by NIH, but one without extensive computational resources as the moment. For more information on the technique, see http://www.cs.cmu.edu/~wcohen/postscript/nips-2009-pic.pdf For more information on the project, see http://www-2.cs.cmu.edu/~wcohen/querendipity/

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目及 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 谱聚类是一种基于对象之间成对相似性的有效且优雅的聚类方法。最近，我开发了一种快速且简单的类似谱聚类的技术，称为幂迭代聚类。与谱聚类一样，点被嵌入到从数据点的相似性矩阵导出的低维子空间中；然而，在谱聚类中，子空间是从亲和力矩阵的拉普拉斯算子的底部特征向量导出的，在我们提出的方法中，子空间是这些特征向量的线性组合的近似。新方法获得了与现有光谱方法相当或更好的聚类，但具有极高的可扩展性，并且非常适合在聚类机器（例如密码子或沃霍尔）上进行并行处理。我想探索使用这种聚类方法来处理与生物学家相关的信息空间（个人信息需求；这是一个由 NIH 资助的项目，但目前没有大量的计算资源。有关该技术的更多信息，请参阅 http://www.cs.cmu.edu/~wcohen/postscript/nips-2009-pic.pdf 有关该项目的更多信息，请参阅 http://www-2.cs.cmu.edu/~wcohen/querendipity/