Divvy: Robust and Interactive Cluster Analysis

Divvy：稳健且交互式的聚类分析

• 批准号: 0963071
• 负责人: Virginia de Sa
• 金额: $ 31万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0963071&HistoricalAwards=false
• 关键词: Divvy; Robust; Interactive; Cluster; Analysis

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.

该项目将开发用于应用快速，健壮和交互式维度降低和聚类算法到现实世界数据集的软件。 该软件（称为Divvy）将提供多维性降低和聚类技术，灵活的域知识集成，可自定义的示例和离群可视化以及使用理论上合理的集群质量测量的群集质量的动态指标的平行可视化。 Divvy将利用并行和图形处理单元计算的最新进展，以便对许多数据集的分区进行接近实时的计算。 Divvy还将用作心理物理研究的平台，研究人类研究人员在数据分析过程中的作用和行为。机动学习技术对于许多不同领域的科学分析越来越重要。 随着数据集的大小和维度的增加，科学家需要访问可以帮助他们快速，轻松执行探索性数据分析和可视化的工具。 Divvy将使用户能够通过直觉接口快速与许多不同维度降低和聚类算法的结果进行可视化。 通过在一个用户友好的界面中收集一系列尖端的机器学习工具，Divvy将为机器学习和相关领域以外的研究人员提供大量改进。 该项目将在机器学习领域外的会议上支持研讨会和教程，以宣扬最近的机器学习技术并鼓励采用Divvy。