Scalable Analysis of Similarity Data

相似性数据的可扩展分析

• 批准号: 0312275
• 负责人: Regina Liu
• 金额: --
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-01 至 2008-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0312275&HistoricalAwards=false
• 关键词: Scalable; Analysis; Similarity; Data

The investigator studies models for the unsupervised clustering and hierarchical organization of objects based on similarity data. The research takes a three-pronged approach to meeting the objective of scaling the analysis to large data sets. First the investigator studies powerful latent variable models with relatively few parameters for analyzing similarity data. Second, the investigator develops dramatically faster algorithms for fitting the models. Specifically, the models are fit with combinatorial variants of the EM algorithm which converge much faster than the conventional EM algorithm. Third, the latent variable structure of the models are extended hierarchically, leading to scalable algorithms which hierarchically cluster previously found clusters of objects. Investigations of this type f latent variable hierarchy lead to models and algorithms which scale to large data sets much better than traditional flat models. The similarity analysis in addition extracts out relationships between clusters, and allows for targeted clustering based on a prior specification of cluster relationships of interest. In this modern data rich age, there is a pressing need for statistical models which can handle large data sets. This investigation focuses on the ubiquitous type of relational data called similarity data, consisting of similarity measurements between pairs of objects. Examples of data which fit into this framework include internet traffic between routers, web connectivity data used by search engines, and microarray gene expression data. There is great interest in finding internet traffic and web topic clusters as well as functional groupings of genes. The investigator studies models and algorithms for clustering and organizational analysis of relational data which can scale to large data sets. The analysis finds meaningful underlying cluster groups along with structural relationships between groups. The methodology the investigator develops has widespread applicability to various disciplines.

研究人员研究了基于相似性数据的对象的无监督聚类和层次组织模型。这项研究采取了三管齐下的方法，以实现将分析扩展到大型数据集的目标。首先，研究者研究了参数相对较少的强大的潜变量模型，用于分析相似数据。其次，研究人员开发了更快的算法来拟合模型。具体地说，该模型适用于EM算法的组合变体，其收敛速度比传统EM算法快得多。第三，模型的潜在变量结构被分层扩展，导致可扩展的算法，该算法分层地对先前发现的对象集群进行聚类。对这种类型的潜在变量层次的研究导致了比传统平面模型更好地扩展到大数据集的模型和算法。此外，相似性分析提取出集群之间的关系，并允许基于感兴趣的集群关系的先前规范进行定向集群。在这个现代数据丰富的时代，迫切需要能够处理大数据集的统计模型。这项研究的重点是被称为相似性数据的无处不在的关系数据类型，它由对象对之间的相似性度量组成。符合这一框架的数据包括路由器之间的互联网流量、搜索引擎使用的网络连接数据和微阵列基因表达数据。人们对寻找互联网流量和网络话题簇以及基因的功能分组非常感兴趣。研究人员研究了可扩展到大型数据集的关系数据的聚类和组织分析的模型和算法。分析发现有意义的基本群组以及群组之间的结构关系。研究人员开发的方法对各种学科具有广泛的适用性。