RadViz Gadget

Description

Creates a RShiny Gadget for RadViz

Usage

RadViz(df, color = NULL)

Arguments

Details

RadViz's goal is to generate a configuration which reveals the underlying nature of the data for cluster analysis, outlier detection, and exploratory data analysis, e.g., by investigating the effect of specific dimensions on the separation of the data. Each dimension is assigned to a point known as dimensional anchors across a unit-circle. Each sample is projected according to the relative attraction to each of the anchors.

It is defined defined for multidimensional numerical data sets X=\{\mathbf{p}_1,\ldots, \mathbf{p}_N\}, for N data points \mathbf{x}_i \in \mathbf{R}^{d} of dimensionality d. Let A =\{ \mathbf{a}_{1}, \dots, \mathbf{a}_{d} \} , be a set of (typically 2D) anchors, each corresponding to one of the d dimensions. The projection \mathbf{p}_i' \in \mathbf{R}^{2}, of a multidimensional point \mathbf{p}_i = (p_{i1},\ldots,p_{id}) \in \mathbf{R}^{d}, in SC is then defined as:

\mathbf{x}_i' = \frac{ \sum_{j=1}^{d} \mathbf{a}_{j} g_j( \mathbf{p}_i)}{\sum_{j=1}^{d} \mathbf{a}_{j} },

with

g_j(\mathbf{p}_i) = \frac{p_{ij} - min_j}{max_j - min_j} ,

and (min_j,max_j),denoting the value range of dimension j. The dimensional anchors can be moved either interactively or algorithmically to reveal different meaningful patterns in the dataset.

Value

A list location of the anchors, coordinates of the projected samples and a logical vector with the selected samples

References

Sharko, J., Grinstein, G., & Marx, K. A. (2008). Vectorized radviz and its application to multiple cluster datasets. IEEE transactions on Visualization and Computer Graphics, 14(6), 1444-1427.

Examples

if (interactive()) {
 library(RadialVisGadgets)
 library(datasets)
 data(iris)
 RadViz(iris, "Species")
}

Star Coordinates Gadget

Description

Creates a RShiny Gadget for Star Coordinates

Usage

StarCoordinates(
  df,
  color = NULL,
  approach = "Standard",
  numericRepresentation = TRUE,
  meanCentered = TRUE,
  projMatrix = NULL,
  clusterFunc = NULL
)

Arguments

Details

Star Coordinate's (SC) goal is to generate a configuration which reveals the underlying nature of the data for cluster analysis, outlier detection, and exploratory data analysis, e.g., by investigating the effect of specific dimensions on the separation of the data. Traditional SC are defined for multidimensional numerical data sets X=\{\mathbf{p}_1,\ldots, \mathbf{p}_N\}, for N data points \mathbf{x}_i \in \mathbf{R}^{d} of dimensionality d. Let A =\{ \mathbf{a}_{1}, \dots, \mathbf{a}_{d} \} , be a set of (typically 2D) vectors, each corresponding to one of the d dimensions. The projection \mathbf{p}_i' \in \mathbf{R}^{2}, of a multidimensional point \mathbf{p}_i = (p_{i1},\ldots,p_{id}) \in \mathbf{R}^{d}, in SC is then defined as:

\mathbf{x}_i' = \sum_{j=1}^{d} \mathbf{a}_{j} g_j( \mathbf{p}_i),

with

g_j(\mathbf{p}_i) = \frac{p_{ij} - min_j}{max_j - min_j} ,

and (min_j,max_j),denoting the value range of dimension j.

In the case of categorical dimensions, the values when numericRepresentation= TRUE are mapped into numerical type i.e. as.numeric() However equally spaced categorical points may not reflect the true nature of the data. Instead, a frequency-based representation may be applied for individual data points. Assuming a categorical dimension j, we calculate the frequency f_{jk}, of each category k of dimension j. The respective axis vector \mathbf{a}_{j}, is then divided into according blocks, whose size represent the relative frequency (or probability) \frac{f_{jk}}{\sum_{l=1}^m f_{jl}}, of each of the m categories of dimension j.

In summary, given an order for each categorical dimension, the Equation g(), above can be extended to SC for mixed data by:

g_j(\mathbf{x}_i) = F_j(x_{ij}) - \frac{P_j(x_{ij})}{2} ,

if categorical/ordinal

g_j(\mathbf{x}_i) = \frac{x_{ij} - min_j}{max_j - min_j} ,

if numerical

where F_j, is the cumulative density function for (categorical/ordinal) dimension j and P_j, its probability function.

Value

A list with the projection matrix, coordinates of the projected samples and a logical vector with the selected samples

References

Kandogan, E. (2001, August). Visualizing multi-dimensional clusters, trends, and outliers using star coordinates. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 107-116).

Lehmann, D. J., & Theisel, H. (2013). Orthographic star coordinates. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2615-2624.

Rubio-Sánchez, M., & Sanchez, A. (2014). Axis calibration for improving data attribute estimation in star coordinates plots. IEEE transactions on visualization and computer graphics, 20(12), 2013-202

Matute, J., & Linsen, L. (2020, February). Hinted Star Coordinates for Mixed Data. In Computer Graphics Forum (Vol. 39, No. 1, pp. 117-133).

Examples

if (interactive()) {
 library(RadialVisGadgets)
 library(datasets)
 data(iris)
 StarCoordinates(iris, "Species")
}