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

当代科学研究面临着日益增长的海量数据。考虑到科学家依赖统计来解释结果，合适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得到回答。这项拟议的研究将探索统计研究的三个方向，以帮助更好地回答其中一些问题。具体地说，开发的技术将有助于识别数据中的群体，或称“集群”。先进的统计分组技术在科学工作的各个领域都很重要。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；金融学；食品真实性；社会科学，特别是在社交网络的建设方面；政治学，特别是在识别投票组方面；以及感官科学，申请者目前正在对分类味觉测试数据进行聚类，以更好地分割感觉空间。例如，这项拟议的研究将帮助科学家通过开发基于基因随时间的行为进行聚类的技术来发现基因的功能。

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.