BSF: 2014391: Aggregation Methods for Partial Preferences Overview.

偏好是属于一群法官的一组项目中的顺序。 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。 对偏好数据的有用且有效的分析通常涉及许多法官之间的各种形式的聚合。这项工作侧重于不完全偏好的分析，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。这项工作将对科学界产生影响，特别是对功能基因组数据的分析（这是生物信息学许多领域的核心）和社会应用产生影响，它将能够高效且有效地分析用户偏好。 该项目开发了针对不完整偏好的数据分析方法。这项工作将建立原理、范式和计算机制，以有效分析不完整偏好的大型数据集。为此，该项目开发了（1）挖掘偏好数据中频繁或其他有趣模式的新方法； (2)新颖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。首席研究员将让研究生和本科生参与她的研究，并将继续与女性和代表性不足的少数族裔合作。 这项研究的过程和结果都将纳入 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.