干旱区生态环境知识资源中心(Arid): Statistically significant relational data mining :

Arid

DOI	10.2172/1204082
报告编号	SAND2014-1105
来源ID	OSTI_ID: 1204082
	Statistically significant relational data mining :
	Berry, Jonathan W.; Leung, Vitus Joseph; Phillips, Cynthia Ann; Pinar, Ali; Robinson, David Gerald; Berger-Wolf, Tanya; Bhowmick, Sanjukta; Casleton, Emily; Kaiser, Mark; Nordman, Daniel J.; Wilson, Alyson G.
英文摘要	This report summarizes the work performed under the project (3z(BStatitically significant relational data mining.(3y (BThe goal of the project was to add more statistical rigor to the fairly ad hoc area of data mining on graphs. Our goal was to develop better algorithms and better ways to evaluate algorithm quality. We concetrated on algorithms for community detection, approximate pattern matching, and graph similarity measures. Approximate pattern matching involves finding an instance of a relatively small pattern, expressed with tolerance, in a large graph of data observed with uncertainty. This report gathers the abstracts and references for the eight refereed publications that have appeared as part of this work. We then archive three pieces of research that have not yet been published. The first is theoretical and experimental evidence that a popular statistical measure for comparison of community assignments favors over-resolved communities over approximations to a ground truth. The second are statistically motivated methods for measuring the quality of an approximate match of a small pattern in a large graph. The third is a new probabilistic random graph model. Statisticians favor these models for graph analysis. The new local structure graph model overcomes some of the issues with popular models such as exponential random graph models and latent variable models.
出版年	2014
报告类型	Technical Report
语种	英语
国家	美国
URL	http://www.osti.gov/scitech/servlets/purl/1204082
资源类型	科技报告
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/270899
推荐引用方式 GB/T 7714	Berry, Jonathan W.,Leung, Vitus Joseph,Phillips, Cynthia Ann,et al. Statistically significant relational data mining :,2014.