3D Grand Tour for multidimensional data and clusters

Abstract. Grand tour is a method for viewing multidimensional data via linear projections onto a sequence of two dimensional subspaces and then moving continuously from one projection to the next. This paper extends the method to 3D grand tour where projections are made onto three dimensional subspaces. 3D cluster-guided tour is proposed where sequences of projections are determined by cluster centroids. Clusterguided tour makes inter-cluster distance-preserving projections under which clusters are displayed as separate as possible. Various add-on features, such as projecting variable vectors together with data points, interactive picking and drill down, and cluster similarity graphs, help further the understanding of data. A CAVE virtual reality environment is at our disposal for 3D immersive display. This approach of multidimensional visualization provides a natural metaphor to visualize clustering results and data at hand by mapping the data onto a time-indexed family of 3D natural projections suitable for human eye's exploration

Document Collection Visualization and Clustering Using An Atom Metaphor for Display and Interaction

Visual Data Mining have proven to be of high value in exploratory data analysis and data mining because it provides an intuitive feedback on data analysis and support decision-making activities. Several visualization techniques have been developed for cluster discovery such as Grand Tour, HD-Eye, Star Coordinates, etc. They are very useful tool which are visualized in 2D or 3D; however, they have not simple for users who are not trained. This thesis proposes a new approach to build a 3D clustering visualization system for document clustering by using k-mean algorithm. A cluster will be represented by a neutron (centroid) and electrons (documents) which will keep a distance with neutron by force. Our approach employs quantified domain knowledge and explorative observation as prediction to map high dimensional data onto 3D space for revealing the relationship among documents. User can perform an intuitive visual assessment of the consistency of the cluster structure

tourrGui: A gWidgets GUI for the Tour to Explore High-Dimensional Data Using Low-Dimensional Projections

This paper describes a graphical user interface (GUI) for the tourr package in R. The tour is a dynamic graphical method for viewing multivariate data. The GUI allows users to interact with a tour in order to explore the data for structures like clustering, outliers, nonlinear dependence. Users can pause the tour, choose a subset of variables, color points by other variables, and switch between several different types of tours

Multivariate volume visualization through dynamic projections

pre-printWe propose a multivariate volume visualization framework that tightly couples dynamic projections with a high-dimensional transfer function design for interactive volume visualization. We assume that the complex, high-dimensional data in the attribute space can be well-represented through a collection of low-dimensional linear subspaces, and embed the data points in a variety of 2D views created as projections onto these subspaces. Through dynamic projections, we present animated transitions between different views to help the user navigate and explore the attribute space for effective transfer function design. Our framework not only provides a more intuitive understanding of the attribute space but also allows the design of the transfer function under multiple dynamic views, which is more flexible than being restricted to a single static view of the data. For large volumetric datasets, we maintain interactivity during the transfer function design via intelligent sampling and scalable clustering. Using examples in combustion and climate simulations, we demonstrate how our framework can be used to visualize interesting structures in the volumetric space