Divvy: Robust and Interactive Cluster Analysis

该项目将开发软件，用于将快速、稳健、交互式的降维和聚类算法应用于现实世界的数据集。该软件名为Divvy，将提供多维降维和聚类技术的并行可视化、灵活的领域知识集成、可定制的范例和离群值可视化，以及使用理论上合理的聚类质量度量的聚类质量动态指标。Divvy将利用并行和图形处理单元计算方面的最新进展，在许多数据集上提供近乎实时的分区计算。Divvy还将被用作心理物理研究的平台，用于调查人类研究人员在数据分析过程中的角色和行为。机器学习技术在许多不同领域的科学分析中越来越重要。随着数据集的大小和维度的增加，科学家需要访问能够帮助他们快速轻松地执行探索性数据分析和可视化的工具。Divvy将允许用户通过一个直观的界面快速交互和可视化许多不同的降维和聚类算法的结果。通过在一个用户友好的界面中收集广泛的尖端机器学习工具，Divvy将为机器学习和相关领域以外的研究人员提供数据分析方法的实质性改进。该项目将在机器学习领域以外的会议上支持研讨会和教程，以宣传最新的机器学习技术并鼓励采用Divvy。

This project will develop software for the application of rapid, robust, and interactive dimensionality reduction and clustering algorithms to real-world datasets. The software, called Divvy, will provide parallel visualization of multiple dimensionality reduction and clustering techniques, flexible domain knowledge integration, customizable exemplar and outlier visualization, and dynamic indicators of cluster quality using theoretically sound cluster quality measures. Divvy will leverage recent advances in parallel and graphics processing unit computing in order to deliver near real-time calculation of partitions on many datasets. Divvy also will be used as a platform for psychophysical studies that investigate the role and behavior of human researchers in the data-analysis process.Machine learning techniques are increasingly essential for scientific analysis in many different fields. As datasets increase in size and dimensionality, scientists need access to tools that can help them quickly and easily perform exploratory data analysis and visualization. Divvy will allow a user to rapidly interact with and visualize the results of many different dimensionality-reduction and clustering algorithms through an intuitive interface. By collecting a broad set of cutting-edge machine-learning tools in one user-friendly interface, Divvy will enable substantial improvements in data analysis methodology for researchers outside of machine learning and related fields. This project will support workshops and tutorials at conferences outside the machine learning field in order to evangelize recent machine learning techniques and encourage adoption of Divvy.