Scalable Analysis of Similarity Data

研究者研究了基于相似数据的对象的无监督聚类和分层组织模型。该研究采用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，研究者研究了具有相对较少参数的强大潜变量模型来分析相似数据。其次，研究者开发了更快的算法来拟合模型。具体来说，该模型是用EM算法的组合变体拟合的，其收敛速度比传统的EM算法快得多。第三，对模型的潜在变量结构进行分层扩展，形成可扩展的算法，该算法对先前发现的对象簇进行分层聚类。对这种类型的潜在变量层次的研究导致模型和算法比传统的平面模型更好地扩展到大型数据集。此外，相似性分析还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目标聚类。在这个数据丰富的时代，迫切需要能够处理大数据集的统计模型。本研究的重点是被称为相似性数据的无所不在的关系数据类型，由对象对之间的相似性度量组成。适合此框架的数据示例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主题集群以及基因的功能分组非常感兴趣。研究者研究了可以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。分析发现有意义的底层集群组以及组之间的结构关系。研究者开发的方法广泛适用于各个学科。

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.