VISUAL AND ANALYTICAL TOOLS FOR CLUSTER ANALYSIS

该子项目是利用NIH/NCRR资助的中心赠款提供的资源的许多研究子项目之一。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。所列机构为中心，不一定是研究者所在机构。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记录级信息的见解。我们的工作将通过设计工具来解决生物医学领域的重要问题，这些工具使科学家能够查看多种聚类方法并分析结果，以帮助数据探索。生物医学领域在过去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。一个新的研究范式是在这一领域中创建的，该领域的方法不同于已确认的研究程序的科学领域。使用视觉技术来利用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遍布该领域。智力上的冒险是这项事业的一部分，因为参与学科的从业人员将不得不相互交流，试图说一种新的共同语言，并在一个与他们自己如此遥远的领域取得突破。本研究将实现三个主要目标：具体目标1。制定、实施和验证多个聚类结果相似性的分析措施（第1年和第2年）：（a）正式定义聚类结果的相似性和不相似性（B）创建新的分析措施，以捕获相似性和不相似性的定义（c）定义和实施分析措施的具体应用;具体目标2。构建新的可视化，帮助识别相似和不相似的记录，集群和子集（3年及以后）（a）设计和实施新的可视化技术，与新的分析措施（B）创建和实施交互方法，促进探索;具体目标3。调查和提高技术和算法的性能，并在大型实验数据集上应用和扩展技术和工具（5年级）。

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 goal of this project is to design new analytical and visual tools and techniques to provide insights into the record level information of multiple clustering algorithm results. Our work is going to address significant 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 decade, generating massive data sets that require new approaches. A new research paradigm is created in this area, and the fields' approaches differ from the established fields of science that have recognized research procedures. Using visual techniques in order to harness the experimental scientist's intuition seems common sense but the strong dependence on automatic computation still pervades the field. Intellectual risk-taking is part of this undertaking, since practicing individuals in participating disciplines would otherwise recourse to interact with one another, attempt to speak a new common language, and make breakthroughs in a field so distant from their own. This research will accomplish three primary goals: Specific Aim 1. Develop, implement and validate analytical measures of similarity of multiple clustering results (Year 1 and Year 2): (a) Formally define the similarity and dissimilarity of clustering results (b) Create novel analytical measures that capture the definitions of similarity and dissimilarity (c) Define and implement specific applications of the analytical measures; Specific Aim 2. Construct novel visualizations that aid the identification of similar and dissimilar records, clusters and subsets (Year 3 and beyond) (a) Design and implement novel visualization techniques that work with the new analytical measures (b) Create and implement interaction methods that facilitate the exploration; Specific Aim 3. Investigate and improve the performance of the techniques and algorithms and apply and extend the techniques and tools on large experimental data sets (Year 5).