Vectorized Radviz and Its Application to Multiple Cluster Datasets

Abstract

Radviz is a radial visualization with dimensions assigned to points called dimensional anchors (DAs) placed on the circumference of a circle. Records are assigned locations within the circle as a function of its relative attraction to each of the DAs. The DAs can be moved either interactively or algorithmically to reveal different meaningful patterns in the dataset. In this paper we describe Vectorized Radviz (VRV) which extends the number of dimensions through data flattening. We show how VRV increases the power of Radviz through these extra dimensions by enhancing the flexibility in the layout of the DAs. We apply VRV to the problem of analyzing the results of multiple clusterings of the same data set, called multiple cluster sets or cluster ensembles. We show how features of VRV help discern patterns across the multiple cluster sets. We use the Iris data set to explain VRV and a newt gene microarray data set used in studying limb regeneration to show its utility. We then discuss further applications of VRV.

Analyzing the Role of Dimension Arrangement for Data Visualization in Radviz

“…This result implies that as the number of dimensions grow, the Radviz technique manages to better maintain the initial distribution of the dataset i.e, the more dimensions, the better the samples can be characterized and the better Radviz will perform. Furthermore, this result confirms previous reports stating that the Radviz technique is useful for highly dimensional datasets [12]. Figure 2(c) shows the visual quality R of the Radviz projections of datasets containing from 5 to 100 different classes.…”

“…Low dimensional data sets have traditionally been represented using either simple line graphs or scatter plots. Nevertheless, in the case of high dimensional data sets, special techniques for data visualization such as Parallel Coordinates [6], Star Glyphs [7], Circle Segments [2] or Radviz [12] are used. One of the key problems of these techniques is the dimension arrangement problem (DA), which evaluates from an algorithmic perspective which arrangement of the dimensions facilitates more the comprehension of the data.…”

Concentric RadViz: Visual Exploration of Multi-task Classification

“…RadViz can map high-dimensional data with thousands of dimensions to the visual space in a very robust manner [2]. Furthermore, RadViz is inherently interactive: the DAs may be moved freely over the circle, thus the mapping can be updated according to user interaction [13].…”

“…Sharko et al [13] proposed the Vectorized RadViz, a methodology that enables the visual evaluation of cluster ensembles. With this technique, it is possible to identify patterns such as similar clusters obtained from different techniques as well as clusters that are unstable in the data set.…”

Multiparametric Analysis of Screening Data: Growing Beyond the Single Dimension to Infinity and Beyond

“…76,77 Yet the potential for HCS data exists, in which one can quickly assess similarity among data points and controls, or the efficiency of hitcalling mechanisms.…”