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。 聚类算法为我们提供了对数据的洞察，发现成员关系，潜在的层次关系，并辅以记录关系。由于存在大量的算法，以及选择适合各种情况和数据集的单一算法的计算不可行性，迫切需要确定解决这个问题的方法。数据的指数增长加剧了这个问题，这推动了对解决这个问题的新方法的需求。 我们致力于设计新的分析和可视化工具和技术，以提供对单个和多个聚类算法结果的见解。我们的工作重点是使生物医学科学家能够分析结果和帮助数据探索的工具，利用可视化技术来呈现数据。 我们正在开发的套件的基本工具可以实现可视化（即散点图，平行坐标）和数据分析（即聚类，radviz）。我们设计并利用了CDSM度量，它将聚类显示为基于此元数据的任何聚类组合或分层树布局的有组织热图可视化。该算法将数据聚类到元组中，并确定每个记录与其他记录共享的最大聚类数。图形布局（线导向或力导向）使用颜色、大小、位置和连接性显示记录的集群。我们扩展到第三个维度的二维显示，使用一些指数和措施，通过一组互动的方法，集群间和集群内的紧密性。我们提出的技术，使记录比较，集群大小比较，邻近分析，集群合并和表示在2D和3D空间。我们使用生物医学数据集显示这些技术的例子。

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. Clustering algorithms provide us with an insight into the data, discovering memberships, potential hierarchical relationships, supplemented by record relationships. Due to the large number of algorithms in existence, and the computational unfeasibility to choose a single algorithm that would suit a variety of situations and data sets, there is a pressing need to identify ways to tackle this problem. This problem is exacerbated by the exponential growth of data which drive the need for new approaches to the problem. We work on designing new analytical and visual tools and techniques to provide insights into the single and multiple clustering algorithm results. Our work focuses on the tools that enable biomedical scientists to analyze results and aid data exploration, utilizing visual techniques to present the data. The basic tools of the suite we are developing enable visualizations (i.e. scatterplots, parallel coordinates) and data analyses (i.e. clustering, radviz). We designed and utilized the CDSM measure that displays clusters as an organized heatmap visualization of any combination of clusterings or a hierarchical tree layout based on this meta-data. The algorithm clusters data into meta-groups and determines the largest number of clusterings that each record shares with other records. Graph layouts (line-oriented or force-directed) show clusters of records using color, size, position and connectivity. We extend the two-dimensional displays into the third dimension using a number of indices and measures for inter- and intra-cluster tightness through a set of interaction methods. The techniques we present enable record comparison, cluster size comparison, proximity analysis, cluster consolidation and representation in 2D and 3D space. We show examples of these techniques using a biomedical data set.