Divvy: Robust and Interactive Cluster Analysis

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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.