Scalable Analysis of Similarity Data

调查研究模型的无监督聚类和层次组织的基础上相似性数据的对象。该研究采取了三管齐下的方法来满足将分析扩展到大型数据集的目标。首先，研究人员研究了功能强大的潜变量模型，用于分析相似性数据的参数相对较少。第二，研究人员开发了更快的算法来拟合模型。具体地说，该模型与EM算法的组合变体相匹配，其收敛速度比常规EM算法快得多。第三，潜在的变量结构的模型层次扩展，导致可扩展的算法，层次聚类以前发现的对象集群。这种类型的潜在变量层次结构的调查导致模型和算法，其规模大的数据集比传统的平面模型好得多。此外，相似性分析提取出聚类之间的关系，并允许基于感兴趣的聚类关系的先验规范进行有针对性的聚类。 在这个现代数据丰富的时代，迫切需要能够处理大型数据集的统计模型。这项调查的重点是无处不在的关系数据类型称为相似性数据，由对象对之间的相似性测量。适合这个框架的数据的例子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。有很大的兴趣在寻找互联网流量和网络主题集群以及基因的功能分组。研究人员研究模型和算法，用于关系数据的聚类和组织分析，可以扩展到大型数据集。该分析发现有意义的基本群集组沿着以及组之间的结构关系。研究者开发的方法对各个学科具有广泛的适用性。

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.