Three directions in model-based clustering, classification and discriminant analysis

基于模型的聚类、分类和判别分析的三个方向

• 批准号: 356040-2011
• 负责人: McNicholas, Paul
• 金额: $ 0.95万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=483013
• 关键词: Three; directions; model; based; clustering

Contemporary science research is confronted with ever-growing masses of data. Given that scientists rely on statistics to interpret results, the availability of suitable statistical methods is essential. Unfortunately, some research questions cannot currently be answered due to the lack of appropriate statistical techniques. The proposed research will explore three directions in statistical research to help better answer some of these questions. Specifically, the techniques developed will aid in the identification of groups within data, or 'clustering'. Advanced statistical clustering techniques are important in a variety of areas of scientific endeavour. The proposed research will facilitate, directly or indirectly, work in areas like: bioinformatics; finance; food authenticity; social science, especially in the construction of social networks; political science, specifically in the identification of voting blocks; and sensory science, where the applicant is currently conducting work on clustering categorical taste testing data to better segment the sensory space. For example, the proposed research will help scientists discover the functions of genes through the development of techniques for clustering genes based on their behaviour over time.

当代科学研究面临着不断增长的海量数据。鉴于科学家依靠统计数据来解释结果，适当的统计方法的可用性是至关重要的。不幸的是，由于缺乏适当的统计技术，一些研究问题目前无法回答。提出的研究将探讨统计研究的三个方向，以帮助更好地回答这些问题。具体来说，开发的技术将有助于识别数据中的组，或“聚类”。先进的统计聚类技术在各种科学领域都很重要。拟议的研究将直接或间接地促进以下领域的工作：生物信息学；金融;食品真实性;社会科学，特别是社会网络建设；政治科学，特别是在投票区块的识别方面；感官科学，申请人目前正在进行分类味觉测试数据的聚类工作，以更好地分割感官空间。例如，拟议的研究将帮助科学家通过发展基于基因随时间的行为的聚类技术来发现基因的功能。