TimeCluster: dimension reduction applied to temporal data for visual analytics

Designing for Interactive Dimension Reduction Visual Analytics Tools to Explore High-Dimensional Data

Exploring high-dimensional data is challenging. As the number of dimensions in datasets increases, the harder it becomes to discover patterns and develop insights. Dimension reduction algorithms, such as multidimensional scaling, support data explorations by reducing datasets to two dimensions for visualization. Because these algorithms rely on underlying parameterizations, they may be tweaked ...

Designing Usable Interactive Visual Analytics Tools for Dimension Reduction

In this paper, we present principles focused on humancentered usability for developing interactive visual analytic systems that enable users to tweak model parameters directly or indirectly so that they may explore high-dimensional data. To exemplify our principles, we refer to our application, Andromeda, that implements interactive weighted multidimensional scaling (WMDS). Through its use, we ...

Dimension Reduction for Visual Data Mining

We present a method for dimension reduction applied to visual data mining in order to reduce the user cognitive load due to the density of data to be visualized and mined. We use consensus theory to address this problem: the decision of a committee of experts (in our case existing attribute selection methods) is generally better than the decision of a single expert. We illustrate the choices op...

Uncertainty-aware Visual Analytics for Spatio-temporal Data Exploration

Visual Hierarchical Dimension Reduction

Traditional visualization techniques for multidimensional data sets, such as parallel coordinates, star glyphs, and scatterplot matrices, do not scale well to high dimensional data sets. A common approach to solve this problem is dimensionality reduction. Existing dimensionality reduction techniques, such as Principal Component Analysis, Multidimensional Scaling, and Self Organizing Maps, have ...