0


合作中的负相关:概念和算法

Negative Correlations in Collaboration:Concepts and Algorithms
课程网址: http://videolectures.net/kdd2010_liu_ncc/  
主讲教师: Qian Liu
开课单位: 南洋理工大学
开课时间: 2010-10-01
课程语种: 英语
中文简介:
本文研究了协作中的负相关的有效挖掘。协作负相关是两组变量之间的负相关,而不是传统上在一对变量之间。它表示一组中所有变量的同步值上升或下降,只要另一组中的所有变量以相反的趋势联合起来。时间复杂度在采矿中是指数级的。我们算法的高效率归因于两个因素:(i)将原始数据转换为二分图数据库,以及(ii)从广泛的事务数据库中挖掘转置闭包。应用酵母基因表达数据,我们通过使用Pearson相关系数和P值,评估协作负相关的生物相关性作为许多现实生活领域中的一个例子。
课程简介: This paper studies efficient mining of negative correlations that pace in collaboration. A collaborating negative correlation is a negative correlation between two sets of variables rather than traditionally between a pair of variables. It signifies a synchronized value rise or fall of all variables within one set whenever all variables in the other set go jointly at the opposite trend. The time complexity is exponential in mining. The high efficiency of our algorithm is attributed to two factors: (i) the transformation of the original data into a bipartite graph database, and (ii) the mining of transpose closures from a wide transactional database. Applying to a Yeast gene expression data, we evaluate, by using Pearson's correlation coefficient and P-value, the biological relevance of collaborating negative correlations as an example among many real-life domains.
关 键 词: 协作负相关; 时间复杂度; 生物相关性
课程来源: 视频讲座网
最后编审: 2019-05-11:lxf
阅读次数: 81