A new variant of radial visualization for supervised visualization of high dimensional data

Radial Visualization technique is a non linear dimensionality reduction method. Radial Visualization projects multivariate data in the 2-dimensional visual space inside the unit circle. Radial Visualization supports display both the samples and the attributes that provides useful information of data structures. In this article, we introduced a new variant of Radial Visualization for visualizing high dimensional data set that named Arc Radial Visualization. The new proposal that modified Radial Visualization supported more space to display high dimensional datasets. Our method provides an improvement in visualizing cluster structures of high dimensional data sets on the Radial Visualization. We present our proposal method with two quality measurements and proved the effectiveness of our approach for several real datasets.

A new variant of radial visualization for supervised visualization of high dimensional data

Radial Visualization technique is a non linear dimensionality reduction method. Radial Visualization projects multivariate data in the 2-dimensional visual space inside the unit circle. Radial Visualization supports display both the samples and the attributes that provides useful information of data structures. In this article, we introduced a new variant of Radial Visualization for visualizing high dimensional data set that named Arc Radial Visualization. The new proposal that modified Radial Visualization supported more space to display high dimensional datasets. Our method provides an improvement in visualizing cluster structures of high dimensional data sets on the Radial Visualization. We present our proposal method with two quality measurements and proved the effectiveness of our approach for several real datasets.

Improved N-dimensional data visualization from hyper-radial values

Higher-dimensional data, which is becoming common in many disciplines due to big data problems, are inherently difficult to visualize in a meaningful way. While many visualization methods exist, they are often difficult to interpret, involve multiple plots and overlaid points, or require simultaneous interpretations. This research adapts and extends hyper-radial visualization, a technique used to visualize Pareto fronts in multi-objective optimizations, to become an n-dimensional visualization tool. Hyper-radial visualization is seen to offer many advantages by presenting a low-dimensionality representation of data through easily understood calculations. First, hyper-radial visualization is extended for use with general multivariate data. Second, a method is developed by which to optimally determine groupings of the data for use in hyper-radial visualization to create a meaningful visualization based on class separation and geometric properties. Finally, this optimal visualization is expanded from two to three dimensions in order to support even higher-dimensional data. The utility of this work is illustrated by examples using seven datasets of varying sizes, ranging in dimensionality from Fisher Iris with 150 observations, 4 features, and 3 classes to the Mixed National Institute of Standards and Technology data with 60,000 observations, 717 non-zero features, and 10 classes.