Order, Spacing, and Clustering in Visual Exploration of Large Scale Data

Worcester Polytechnic Institute, Worcester MA

Investigators

Abstract

The focus of this research project is to develop visualization, interaction, and data management technologies to address the problems of high dimensionality and data type heterogeneity in large-scale visual data mining. The basic approach is to apply multiresolution clustering strategies across the dimensions of a data set as well as within individual dimensions containing nominal or categorical values, and exploit the ordering and positioning of data axes and data points to emphasize relationships within the data. For visualization, the tasks involve the development of methods for determining ordering and variable spacing within and between data and dimensions as well as clustering of dimensions into multi-resolution abstractions, and integrating them into several existing multivariate display techniques. For interaction, tools for intuitive navigation and exploration within the multiresolution spaces are developed. This includes interactive reclustering tools to allow users to guide the process of splitting and grouping clusters of data objects and dimensions. For data management the tasks involve the development of high-dimensional indexing and multi-resolution data view management for high-dimensional data access, and caching and prefetching strategies to support real-time visual exploration. The ease of use as well as performance of the display and interactive tools over large data sets is assessed. The results of this research will provide data analysts in domains such as bioinformatics, earth and space sciences, and e-commerce the ability to interactively explore the increasingly large and complex data sets being generated.