BSF: 2014391: Aggregation Methods for Partial Preferences Overview.

偏好是一组归因于法官群体的项目中的顺序。 偏好数据有多种形式，例如排名列表和成对比较，并且在不同领域的大量应用程序中无处不在。 对偏好数据的有用和有效分析通常涉及许多法官之间的各种形式的汇总。这项工作的重点是分析不完全的偏好，包括挖掘，聚类和聚合。 作为该项目一部分开发的模型、算法、数据和软件产品将公开提供。这项工作将对科学界产生影响，特别是对功能基因组学数据的分析产生影响，因为这是生物信息学许多领域的核心，并对社会应用产生影响，因为它将有助于对用户的偏好进行高效率和有效的分析。 该项目开发了针对不完全偏好的数据分析方法。这项工作将建立原则，范例，和计算机器的有效分析大型数据集的不完整的偏好。为此，该项目开发了（1）挖掘偏好数据中频繁或有趣模式的新方法;（2）新的聚类和偏好聚合方法;（3）广泛的数据采集和实验评估，以加强分析方法的研究和开发。PI将让研究生和本科生参与她的研究，并将继续与妇女和代表性不足的少数民族合作。 这项研究的过程和结果都将被整合到PI教授的数据管理和数据科学课程中。

Preferences are orders among a collection of items attributed to a population of judges. Preference data comes in a variety of forms,such as ranked lists and pairwise comparisons, and is ubiquitous in a plethora of applications across different domains. Useful and effective analysis of preference data typically involves various forms of aggregation among many judges. This work focuses on the analysis of incomplete preferences, including mining, clustering and aggregation. Models, algorithms, data and software products developed as part of this project will be made publicly available. The work will have an impact on the scientific community, in particular on the analysis of functional genomics data, which is central to many areas of bioinformatics, and on social applications, where it will enable efficient and effective analysis of user preferences. This project develops data analysis methodologies that are geared towards incomplete preferences. This work will establish principles, paradigms, and computing machinery for effective analysis of large datasets of incomplete preferences. To that aim, this project develops (1) novel approaches for mining frequent, or otherwise interesting, patterns in preference data; (2) novel clustering and preference aggregation methods; and (3) an extensive data acquisition and experimental evaluation to enhance the research and development of analysis methodologies. The PI will involve graduate and undergraduate students in her research, and will continue to work with women and under-represented minorities. Both the process and the outcome of this research will be integrated into data management and data science courses taught by the PI.