VISUAL AND ANALYTICAL TOOLS FOR CLUSTER ANALYSIS

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 聚类算法的输出是聚类及其成员关系的列表，在某些情况下，由记录关系（如邻近值、连接）补充。该项目的目标是设计新的分析和可视化工具和技术，以提供对多个聚类算法结果的见解。我们的工作通过设计工具来解决生物医学领域的问题，这些工具使科学家能够通过多种聚类方法来分析结果，以帮助数据探索。生物医学领域在过去二十年中经历了强劲的增长，产生了大量的数据集，需要新的方法。利用视觉技术来驾驭实验科学家的直觉似乎是常识，但对自动计算的强烈依赖仍然存在于该领域。该项目有三个具体目标： I.开发、实施和验证多个聚类结果相似性的分析措施： - 形式化定义聚类结果的相似性和相异性 - 确定新的分析措施，捕捉相似性和不相似性的定义 - 确定新的分析措施，捕捉相似性和不相似性的定义 - 定义和实施分析措施的具体应用 二.用于识别相似和不相似记录、聚类和子集的可视化： - 设计并实现与新分析度量一起使用的可视化技术 - 创建并实现便于探索的交互方法 三.研究和改进技术和算法的性能，并在大型实验数据集上应用和扩展技术和工具

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The output of a clustering algorithm is a list of clusters and their membership, in some cases supplemented by record relationships, such as proximity values, connections. The goal of this project is to design new analytical and visual tools and techniques to provide insights into the multiple clustering algorithm results. Our work addresses questions in the biomedical field by devising tools that enable scientists to look through multiple clustering methods and analyze the results, in order to aid the data exploration. The biomedical field has undergone strong growth over the past two decades, generating massive data sets that require new approaches. Using visual techniques to harness the experimental scientists intuition seems common sense but the strong dependence on automatic computation is still present in the field. This project is identified by three specific aims: I. Develop, implement and validate analytical measures of similarity of multiple clustering results: - Formally define the similarity and dissimilarity of clustering results - Identify new analytical measures that capture the definitions of similarity and dissimilarity - Identify new analytical measures that capture the definitions of similarity and dissimilarity - Define and implement specific applications of the analytical measures II. Visualizations for identification of similar and dissimilar records, clusters and subsets: - Design and implement visualization techniques that work with the new analytical measures - Create and implement interaction methods that facilitate the exploration III. Investigate and improve the performance of the techniques and algorithms and apply and extend the techniques and tools on large experimental data sets